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16 April 2021
9:47
XYCarto: RGB Elevation Creation for 3D Online Mapping (Terrain RGB)
sur Planet OSGeo
Mt. Ruapehu, Ngauruhoe, and Tongariro
The following is about how to build an elevation file to work with procedural-gl.js. I just really wanted to build my own elevation dataset and thought it would be helpful to share how I did it.

Procedural-gl.js has changed the game in attainable 3D online mapping. When I came across this project, I was very excited and wanted to get to mapping ASAP. There is so much potential in just a few lines of JS code; just add a base map, an elevation file and boom, you have a smooth functioning 3D map, mobile ready. I cannot thank Felix Palmer enough for putting this out there. You can check out the source code and some really great maps he developed here.

I’m not one to leave well enough alone. I wanted to develop a method to use New Zealand specific elevations with my goal being to eventually incorporate New Zealand’s LiDAR elevations. However, before I get to building elevation models for LiDAR, I wanted to test building an elevation model with the LINZ 8m DEM. This dataset covers the entire nation and was light weight enough to test on the ol’ home Linux box.

Method

As mentioned above, to run procedural-gl.js in its most basic form, you’ll need a tile cache for your base map and a tile cache for your elevation file. The base map I have covered, but I was unfamiliar for what was needed for the elevation tile cache. Fortunately, I came across SyncPoints tutorial on how to do this.

I am not going to rewrite their entire process. They did a really good job explaining the what, why, and how. Instead, I will layout what I did for the NZ specific data. Much of this process can be further refined and this is really just the proof of concept.

Two quick notes before I get started:
1. For now, this process is only done in Web Mercator. I always want to push the limits and do my online work in NZTM, but for now, I am going to work with what I have.
2. I am not going to talk about building a raster tile cache for the base map. I was fortunate to have a web mercator tile cache for NZ available.
We want to create an elevation file, in PNG rgb format, rendered into an XYZ tile cache directory, with 512x512px tiles.

The basic steps are this:
1. Remove all errant noData pixels from a geotiff.
2. Convert your elevation geotiff to rgb (single band to three band), in Byte format.
3. Render your rgb geotiff into xyz raster tiles. Use a directory format.
4. Consume through procedual-gl.js
List of Tools
- procedural-gl.js
- gdalbuildvrt
- gdalwarp
- rio rgbify
- gdal2tiles
- npm
Data

The data I used for this was the full LINZ 8m DEM elevation. Make sure your data is in Web Mercator projection (EPSG:3857). See some earlier blogs about how to retroject across all the tiles using BASH and gdalwarp.

LINZ 8m DEM

Process

1. With the VRT, we can gather all the elevation tiles under one file name.

gdalbuildvrt elevation.vrt *.tif

2. Remove nodata

Using the VRT, we can preform the noData conversion across all the elevation tiles as if it were a mosaic. Better yet, the output from this command will produce a mosaic out the other side. GDAL is magic.

gdalwarp -t_srs EPSG:3857 -dstnodata None -co TILED=YES -co COMPRESS=DEFLATE -co BIGTIFF=YES elevation.vrt elevation_noData_mosaic.tif

3. RGB-ify the elevation mosaic.

rio rgbify -b -10000 -i 0.1 /elevation_noData_mosaic.tif elevation_noData_mosaic_rgb.tif

4. Create your XYZ tile cache in EPSG:3857

GDAL2Tiles is a quick way to render xyz tile caches if you have single layer and are working in Web Mercator. Be sure to use 3.1 or greater to get the functionality of xyz caching and tile size control. You need these both. In the end, I used the GDAL Docker developed by perrygeo, with modification, --with-python, and got access to GDAL 3.2. It was a lifesaver, however, I did need to modify the Dockerfile to add GDALs Python bindings and rebuild the Docker.

gdal2tiles.py --s_srs=EPSG:3857 --zoom=0-16 --xyz --tilesize=512 --processes=7 elevation_noData_mosaic_rgb.tif /data/tile-cache

Localhost Testing

Once I built the tile cache, I built a quick site on my localhost to test. I embedded the tile cache into the site (place the tile cache in the same directory as your web files), then I ran it under a local server. The advantage of embedding your tile cache for testing is that it allows the local server to serve out the raster tiles as well. There is no need to set up a tile server. The local server does this for you.

I switched it up this time used the npm [http-server] to avoid some cors issues I was encountering.

http-server . —cors 8000

Code and Website

You can view the code for the HTML and JS here:

[https:]]

The running website can be found here:

[https:]]

It works great on mobile devices, but will happily display on your desktop.
15 April 2021
12:00
CARTO Blog: Visualizing COVID-19 Vaccination Progress at the Individual Level
sur Planet OSGeo

This time last year, the race to create a COVID-19 vaccine began. Many experts, including Dr. Anthony Fauci, the Director of The National Institute of Allergy and Infectiou...
11:00
CARTO Blog: Aena's Indoor Mapping & Routing App Expands to Barcelona
sur Planet OSGeo

Last year thanks to a collaboration between Aena, Telefonica, CARTO, and Situm a new Indoor Mapping solution called ‘AenaMaps’ was launched to assist passengers during thei...
14 April 2021
11:00
CARTO Blog: Retail Site Selection: Complimentary Post Pandemic Playbook
sur Planet OSGeo

Brick and mortar retail has had winners and losers since the COVID pandemic began. Some brands have pivoted effectively, whilst others have struggled to navigate lockdowns,...
12 April 2021
17:58
GeoSolutions: State of GeoNode Free Webinar – Release 3.2
sur Planet OSGeo

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16:07
Narcélio de Sá: A extensão dos limites dos municípios do Ceará
sur Planet OSGeo

Pegando inspiração no artigo do @NexoJornal , sobre a extensão das divisas dos estados brasileiros, eu resolvi replicar a análise para os municípios do Ceará.

O Governo do Estado do Ceará publicou a Lei Nº16.821, de 09 de janeiro de 2019, onde ficam descritos os limites intermunicipais dos 184 municípios cearenses.

Recentemente em um esforço coletivo a comunidade OpenStreetMap do Ceará finalizou os ajustes dos limites na base do OSM.
Top 5 Maiores limites entre municípios do Ceará.
1º – Limite entre Parambu e Tauá com 124,24 km

2º – Limite entre Pereiro e Jaguaribe com 120,99 km

3º – Limite entre Independência e Tauá com 111,05 km

4º – Limite entre Ibicuitinga e Morada Nova com 97,53 km

5 º – Limite entre Santa Quitéria e Catunda com 88,24 Km

Top 5 Menores limites entre municípios do Ceará.
1º – Limite entre Horizonte e Guaiúba com 803.21m

2º – Limite entre Limoeiro do Norte e São João do Jaguaribe com 849.08m

3º – Limite entre Canindé e Madalena com 937.71m

4º – Limite entre Aquiraz e Fortaleza com 1,71 Km

5º – Limite entre Pedra Branca e Quixeramobim com 1,73 Km

A análise foi realizada no @qgis, onde foi criado um modelo que analisa os dados dos limites municipais e retorna uma camada dos limites entre os municípios com as respectivas extensões.

Também no QGIS foi possível elaborar todos os 467 mapas dos limites utilizando a função Atlas do QGIS. Em breve iremos publicar um vídeo mostrando como chegamos nesse resultado.

Os dados de limites do estado do Ceará pode ser obtidos no portal do IPECE ou diretamente do OSM, segue um link do overpass turbo onde os dados podem ser visualizados e baixados: overpass-turbo.eu/s/160y

Para saber um pouco mais sobre a comunidade OpenstreetMap e como contribuir com o projeto segue um fio no twitter:

1 – Nesse fio irei dar algumas dicas para quem quer começar a mapear no @openstreetmap , o maior projeto de mapeamento aberto do Mundo.

Pra começar tu tem que criar tua conta no OSM.
Isso pode ser feito nesse endereço: [https:]]
… pic.twitter.com/qiLi9t1m7w
— Narcélio de Sá (@NarceliodeSa) April 11, 2021

The post A extensão dos limites dos municípios do Ceará appeared first on Narcélio de Sá.
06 April 2021

8:00

Lutra consulting: Tips & tricks for point clouds in QGIS

sur Planet OSGeo

Just few weeks ago, QGIS 3.18 has been released - the first version to include support for point cloud data, thanks to the great support from the QGIS community in our joint crowdfunding campaign with North Road and Hobu.

We have received a lot of feedback from users and we would like to summarise the most common problems people have faced, and add a couple of tips on useful features that are otherwise easy to overlook.

1. I am unable to load LAS/LAZ files

If your QGIS installation does not recognize .las or .laz files (which are the two point cloud formats that we currently support in QGIS), the most likely source of the problem will be that the PDAL library needed for reading LAS/LAZ is missing in your installation. Unfortunately not all installers include it at this point. Particularly on Windows, there are several options how to install QGIS, and only one choice is the right one for PDAL support. At the time of writing (April 2021), you need to download installer from section called “Standalone installers from OSGeo4W testing packages (MSI)”, here’s a screenshot of what to look for:

On macOS the official all-in-one installers include point cloud support. On Linux, PDAL support depends on the particular distribution/packages, but probably most of them include point PDAL library.

2. Point cloud is completely flat in 3D

It may happen that you load a point cloud layer, then open the 3D map view and the point is displayed as a flat surface like on this screenshot:

The reason for this is that the 3D renderer is not enabled for your point cloud layer. To enable 3D renderer, open the Layer Styling panel (there’s F7 shortcut to open it!), then switch to the second tab (“3D View” - with a cube icon) and change the “No Rendering” option to some other option - for example “Classification” in case your point cloud is classified. You should then see your point cloud in 3D.

3. Point cloud is rendered twice - in 3D and in 2D (“flat”)

Commonly when people open 3D view with a point cloud, they may see the point cloud rendered twice, like in the following screenshot:

The reason is that both 3D rendering and 2D rendering of point cloud is enabled, and therefore the layer is also rendered as a 2D map texture on top of terrain (which is flat by default). An easy way how to fix this is to set 2D rendering of the point cloud layer to “Extent Only” in Layer Styling panel (in the first tab):

If the dashed rectangle marking the extent is still bothering you, it is possible to change the line symbol to use white (or transparent) colour.

Hopefully in near future we would address unexpected behaviour and layers with a 3D renderer defined would not be rendered as 2D.

4. I still can’t see my point cloud in 3D view

It could happen that if your point cloud is for a small area, yet the elevation of points is relatively high: when you first open 3D view or when you click “Zoom Full” button, the view may get zoomed too close and the actual point cloud data may be behind the camera. Try zooming out a bit to see if it helps. (This is a bug in QGIS - at this point “zoom full” ignores extra entities and only takes into account terrain.)

5. Enable “Eye Dome Lighting” in 3D view

For a much better 3D perception of your point cloud, try clicking the “Options” button (with a wrench icon) in the toolbar of 3D view and enable “Show Eye Dome Lighting” in the pop-up menu. This will apply extra post-processing that adds slight shading based on the positions of nearby points, and adds silhouettes when there is a sudden change in depth:

As soon as you zoom into your point cloud more, to the point when individual points can be seen, the eye dome lighting effect will start to disappear. You can try experimenting with the point size (in Layer Panel, 3D View tab) - increasing the point size will help.

- A view with point size 2.0 pixels

- The same view with point size 6.0 pixels

6. Try the new camera navigation mode in 3D view

In QGIS 3.18 we have added a new “Walk Mode” camera navigation mode that is much better suited for inspection of point clouds (compared to the default “Terrain Based” camera navigation mode). Open 3D map view configuration dialogue, pick “Camera & Skybox” tab and set it here:

Control	Action
Mouse move	Rotate camera
Mouse wheel	Change movement speed
W / Up	Move forward
S / Down	Move backward
A / Left	Move left
D / right	Move right
Q / Page up	Move up
E / Page dn	Move down

7. Use elevation scaling and offset to your advantage

Sometimes it is useful to modify offset and/or scale of the elevation of points (Z values). For example, if the point elevations do not match your other 3D data, or maybe you have source data where X,Y coordinates are in meters and Z coordinate is in feet!

Another case when this can be useful, is when your point cloud data are further away from the terrain and the default “Terrain Based” navigation mode does not work nicely - it expects that data are near the terrain, and the camera rotates around a point terrain, which may feel strange when browsing point clouds. A workaround is to apply offset to the point cloud layer to move the points near the terrain. For example, this is a point cloud which is roughly at 200 meters elevation (the grey plane is the terrain):

When an offset of -200 is applied to the point cloud in Layer Styling panel, data show up much closer to the terrain and camera navigation feels more natural:

8. Try circular points in 2D maps

By default QGIS draws points as squares as this is the fastest option. But for higher quality output you may want to change point symbol style to circle in Layer Styling panel, which makes things look a little less jagged:

– using squares – using circles

At this point we always use square points in 3D views - in the future we will likely offer circular points in 3D views as well.

9. Give us your feedback

We would love to hear from you about your experience with point clouds in QGIS so far, what features you are missing or what problems you have encountered - feel free to drop us a mail at info@lutraconsulting.co.uk. If you think you have found a bug, best to file an issue for it in the QGIS GitHub repository.

With the QGIS 3.18 release, we know we are only at the beginning of a long journey to provide great point cloud support to the users. The decreasing cost of laser scanning (LIDAR) hardware and increasing availability of photogrammetric methods means that we will be be seeing point cloud data in GIS more and more often. There is still a lot of functionality missing for efficient work with raw point clouds, for easy processing of data and we are still using only a fraction of what the PDAL point cloud library offers. We are dedicated to provide first class support for point clouds in QGIS - however this cannot be done without funding. Therefore, if you would like to help us to push point clouds in QGIS to the next level, please do not hesitate to contact us at info@lutraconsulting.co.uk.

01 April 2021
14:52
Narcélio de Sá: What2figures um sistema de endereçamento global revolucionário
sur Planet OSGeo

WTF (what2figures) é um novo sistema de endereçamento global revolucionário, que pode localizar sua posição na Terra com apenas dois números.

No passado, se você quisesse compartilhar sua localização com outra pessoa, teria que se lembrar de três palavras inteiras. Obviamente, ter que lembrar três palavras desconexas para compartilhar sua localização é desnecessariamente complicado, menos para o o Goku que usa carne, leite, pão até para sincronizar um Kamehameha. Agora você só precisa se lembrar de dois números simples!
Sim meu jovem, apenas dois números.
E isso não é tudo … WTF é reconhecido por todas as principais empresas de mapeamento em todo o mundo. Insira seu endereço WTF de dois números no Google Maps, OpenStreetMap, Bing Maps ou Apple Maps e seu endereço será reconhecido instantaneamente e mostrado no mapa.

Encontrar seu endereço no what2figures não poderia ser mais fácil. Basta clicar no mapa WTF interativo e você receberá um endereço de dois números exclusivo. Este endereço de dois dígitos aponta sua localização na Terra com precisão milimétrica. Agora você pode compartilhar seu endereço WTF com quem quiser. Clique nos links do Google Maps ou OSM e seu endereço WTF será mostrado no Google Maps ou OpenStreetMap. Se você deseja compartilhar sua localização atual com o resto do mundo, você também pode clicar no link ‘Tweet’ para postar seu endereço WTF de dois números em seu fluxo do Twitter.

Use WTF e nunca mais se perca.

Fonte: Maps Mania

The post What2figures um sistema de endereçamento global revolucionário appeared first on Narcélio de Sá.
10:00
Paul Ramsey: Dumping a ByteA with psql
sur Planet OSGeo
Sometimes you just have to work with binary in your PostgreSQL database, and when you do the bytea type is what you’ll be using. There’s all kinds of reason to work with bytea:
- You’re literally storing binary things in columns, like image thumbnails.
- You’re creating a binary output, like an image, a song, a protobuf, or a LIDAR file.
- You’re using a binary transit format between two types, so they can interoperate without having to link to each others internal format functions. (This is my favourite trick for creating a library with optional PostGIS integration, like ogr_fdw.)
Today I was doing some debugging on the PostGIS raster code, testing out a new function for interpolating a grid surface from a non-uniform set of points, and I needed to be able to easily see what the raster looked like.

There’s a function to turn a PostGIS raster into a GDAL image format, so I could create image data right in the database, but in order to actually see the image, I needed to save it out as a file. How to do that without writing a custom program? Easy! (haha)

Basic steps:
- Pipe the query of interest into the database
- Access the image/music/whatever as a bytea
- Convert that bytea to a hex string using encode()
- Ensure psql is not wrapping the return in any extra cruft
- Pipe the hex return value into xxd
- Redirect into final output file
Here’s what it would look like if I was storing PNG thumbnails in my database and wanted to see one:
```
echo "SELECT encode(thumbnail, 'hex') FROM persons WHERE id = 12345" \
  | psql --quiet --tuples-only -d dbname \
  | xxd -r -p \
  > thumbnail.png
```
Any bytea output can be pushed through this chain, here’s what I was using to debug my ST_GDALGrid() function.
```
echo "SELECT encode(ST_AsGDALRaster(ST_GDALGrid('MULTIPOINT(10.5 9.5 1000, 11.5 8.5 1000, 10.5 8.5 500, 11.5 9.5 500)'::geometry, ST_AddBand(ST_MakeEmptyRaster(200, 400, 10, 10, 0.01, -0.005, 0, 0), '16BSI'), 'invdist' ), 'GTiff'), 'hex')" \
  | psql --quiet --tuples-only grid \
  | xxd -r -p \
  > testgrid.tif 
```
29 March 2021
18:29
geomati.co: Geomatico en las noticias de TMB
sur Planet OSGeo

Transports Metropolitans de Barcelona (TMB) se hace eco en sus noticias de la aplicación interna desarrollada por TMB y Geomatico que ofrece una visualización 3D y en tiempo real de la circulación en las líneas de metro de Barcelona
[https:]]

La entrada Geomatico en las noticias de TMB se publicó primero en Geomatico.
11:31
gvSIG Team: Análisis hidrológico en la subcuenca Charanal usando como herramienta el software libre gvSIG
sur Planet OSGeo

Compartimos una tesis final de grado que puede ser muy interesante para ampliar conocimientos en el uso de gvSIG como herramienta de análisis hidrológico.

Una introducción a lo que podéis encontrar en esta tesis, redactada por su autora:

«Piura es una ciudad que cada cierto tiempo es fuertemente golpeada por las consecuencias de las intensas precipitaciones que se presentan en nuestra región, especialmente en épocas del Fenómeno El Niño. La presente tesis surge con la intención de conocer las características físicas y el aporte de caudal de una de las subcuencas de gran aporte al caudal del río Piura en épocas del FEN, además de conocer que tan bien puede trabajar un software libre como lo es gvSIG en comparación con un software licenciado cuya limitación de este último se encuentra en el elevado costo de su licencia, un requisito que no todos pueden cumplir.

Por último, el autor desea que el material puesto a disposición sea de utilidad para aquellos que tengan la intención de llevar a cabo proyectos que impliquen un estudio profundo en hidrología».

La tesis está accesible en PDF en este enlace: [https:]]
11:00
Mapgears: Paving the way to the next generation of mobile location technology
sur Planet OSGeo

Daniel Morissette, president of Mapgears, shows the mobile app used by over 90 000 snowmobilers and ATVers across North America every year.

After two years of partnership that led to the conquest of the North American snowmobile and ATV trail management market, Mapgears acquires Ondago, a transaction that will push the Canadian firm to new heights by adding a solid mobile development expertise to the team.
A natural match

Daniel Morissette, Simon Mercier and Julien-Samuel Lacroix from Mapgears’ management team with their new collaborators from Ondago, Benoit Racine and Martin Guay

Mapgears is an expert in web mapping that has developed numerous tools to simplify the monitoring of operations and decision-making process of its clients around the world. The firm currently has customers in a wide variety of sectors, including marine navigation, public works, recreational trails and many others.

In order to meet the evolving needs of its clients and solidify its presence in the market, Mapgears quickly turned to the mobile technologies and sought a partner with solid experience and a proven product in this market.

This is where the collaboration with Igloo Creations, the company behind the Ondago product which is already extremely popular in the Quebec tourism industry, was born. This product consists of a mapping platform and a mobile application available on iPhone, iPad and Android which allows users to easily locate themselves during their expeditions.

This relationship has evolved very naturally over the past two years due in large part to the complementary nature of the technologies and business processes, the similar needs of the customers and the in-depth knowledge of each team in their field.

With a combined total of more than a quarter of a million active users for mobile applications and hundreds of clients in the public and private sectors, the new Mapgears team now stands as a key player in mapping-based solutions in North America.
Improvements and new products to come
In a more concrete manner, existing Mapgears customers will have access to a well-rounded range of solutions and increased portability of their mapping applications that will now be available on mobile. The development of new functionalities will also be faster thanks to the addition of new talents to the team.

Customers of the mapping platform and users of the Ondago mobile application will be reassured to know that the original team will continue to provide the excellent level of service they are used to. They will also benefit from Mapgears’ expertise, which will greatly help in improving existing products and will lead to many new features.
We are hiring!
In order to support the rapid growth that is expected following this transaction, Mapgears is actively looking to recruit experienced mobile application developers who are looking for a challenge!
9:45
gvSIG Team: gvSIG Desktop aplicado a Geología
sur Planet OSGeo
Os traemos un interesante tutorial de gvSIG Desktop aplicado a Geología, resultado de las prácticas profesionales de Scarlet Pilar Flores Mancilla, alumna de la UAEMEX que, como otros alumnos de distintas universidades del mundo, ha realizado sus prácticas en la Asociación gvSIG. En este caso, las prácticas han estado tutorizadas principalmente por Carlos Revuelto, de la empresa Geoscan.

El tutorial hace un recorrido formativo que se estructura en el siguiente contenido:
- Descarga: Modelo Digital de Elevación
- Descarga: Ortofotos
- Descarga: Geología
- Geoprocesos con un Modelo Digital de Elevaciones: Perfiles
- Geoprocesos con un Modelo Digital de Elevaciones: Secciones Transversales
- Geoprocesos con un Modelo Digital de Elevaciones: A través de curvas de nivel
- Generación de una capa de curvas de nivel a partir de un MDR
- Análisis hidrológico
- Cálculo de la red de drenaje
- Cálculo de las cuencas vertientes
- Caracterización de cuencas
- Cálculo de cuenca hidrográfica
- Modelización: vectorización de cuencas y obtención de propiedades geométricas
- Cálculo del tiempo de concentración
- Métodos de cálculo de tiempo de concentración
- Cálculo de tiempo de salida
Esperemos que el trabajo de Scarlet os resulte de utilidad. Por nuestra parte agradecemos su dedicación e interés por documentar y compartir sus prácticas.

Descargar tutorial
2:00
EOX' blog: The data triangle in the AMiDA project
sur Planet OSGeo

We are about to finalize an ESA project called AMiDA, and thus I think it is a good moment to recapitulate and look back at what things we have achieved and explored technology-wise during this project. Just a very quick introduction, the project was lead by SISTEMA and participating in the activity ...
28 March 2021
18:21
Free and Open Source GIS Ramblings: Movement data in GIS #35: stop detection & analysis with MovingPandas
sur Planet OSGeo
In the last few days, there’s been a sharp rise in interest in vessel movements, and particularly, in understanding where and why vessels stop. Following the grounding of Ever Given in the Suez Canal, satellite images and vessel tracking data (AIS) visualizations are everywhere:

The 224,000-ton shipping vessel, #EverGiven, seen here in this WorldView-2 #satellite image from March 26, 2021, blocking one of the world’s busiest waterways, the #SuezCanal, since Tuesday. pic.twitter.com/KDLoCqX1w8
— Maxar Technologies (@Maxar) March 26, 2021

Animation showing how the grounded container ship brought the Suez Canal to a standstill. Huge thanks to @VesselsValue for supplying the data.

Read @OilSheppard @harrydemps & @hebamks story [https:]] #gistribe #dataviz pic.twitter.com/JRkwmhG0KJ
— Steven Bernard (@sdbernard) March 24, 2021

Using movement data analytics tools, such as MovingPandas, we can dig deeper and explore patterns in the data.

The MovingPandas.TrajectoryStopDetector is particularly useful in this situation. We can provide it with a Trajectory or TrajectoryCollection and let it detect all stops, that is, instances were the moving object stayed within a certain area (with a diameter of 1000m in this example) for a an extended duration (at least 3 hours).
```
stops = mpd.TrajectoryStopDetector(trajs).get_stop_segments(
    min_duration=timedelta(hours=3), max_diameter=1000)
```
The resulting stop segments include spatial and temporal information about the stop location and duration. To make this info more easily accessible, let’s turn the stop segment TrajectoryCollection into a point GeoDataFrame:
```
stop_pts = gpd.GeoDataFrame(columns=['geometry']).set_geometry('geometry')
stop_pts['stop_id'] = [track.id for track in stops.trajectories]
stop_pts= stop_pts.set_index('stop_id')

for stop in stops:
    stop_pts.at[stop.id, 'ID'] = stop.df['ID'][0]
    stop_pts.at[stop.id, 'datetime'] = stop.get_start_time()
    stop_pts.at[stop.id, 'duration_h'] = stop.get_duration().total_seconds()/3600
    stop_pts.at[stop.id, 'geometry'] = stop.get_start_location()
```
Indeed, I think the next version of MovingPandas should include a function that directly returns stops as points.

Now we can explore the stop information. For example, the map plot shows that stops are concentrated in three main areas: the northern and southern ends of the Canal, as well as the Great Bitter Lake in the middle. By looking at the timing of stops and their duration in a scatter plot, we can clearly see that the Ever Given stop (red) caused a chain reaction: the numerous points lining up on the diagonal of the scatter plot represent stops that very likely are results of the blockage:

Before the grounding, the stop distribution nicely illustrates the canal schedule. Vessels have to wait until it’s turn for their direction to go through:

You can see the full analysis workflow in the following video. Please turn on the captions for details.

Huge thanks to VesselsValue for supplying the data!

For another example of MovingPandas‘ stop dectection in action, have a look at Bryan R. Vallejo’s tutorial on detecting stops in bird tracking data which includes some awesome visualizations using KeplerGL:
Kepler.GL visualization by Bryan R. Vallejo
This post is part of a series. Read more about movement data in GIS.
25 March 2021
14:06
GIScussions: The Locus Charter
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Photo by Pixabay on Pexels.com
Yesterday I sat in on the launch of the Locus Charter by the Benchmark Initiative. I was more than a little sceptical about yet another initiative in the location space, I have listened to national strategy launches, addressing initiatives, new community/organisational launches and open data policies and commitments. Most of these have over promised and under delivered or fizzled out – I think the Locus Charter may be the exact opposite.

The Locus Charter is

“a proposed international set of principles and guidance for ethical and responsible practice when using location data.”

It sets out 10 simple principles that organisations are encouraged to consider when creating, collecting and using location data.
1. People should understand and be aware when their location information is being collected
2. Personally identifiable location data should be respected, protected and used with informed consent
3. Good location data practice adheres to the data minimisation principle
4. The same rights that people have in the physical world must be protected in the digital world
5. When collecting location data relating to vulnerable people and places one should take care to balance the benefits being sought with the potential for harm
6. Care should be taken to understand bias in the data that is collected
7. The more context data that is combined with location data the more powerful. Measures should be put in place to prevent identification of a persons location
8. The individual or collective location data pertaining to a people, flora or fauna should not be used to discriminate, exploit or harm
9. People should have access to what location data is being collected about them
10. Avoid undue intrusions into people’s lives
These principles seem pretty unarguable to me, sort of the motherhood and apple pie of geo-ethics but I can foresee them being challenging for some data collectors and business models. Setting out principles and encouraging businesses to evaluate their current data products and plans against these principles is a great way to start rather than pushing for regulation or legislation immediately. If the industry leaders (MAGFA, I’m looking at you) come on board with these principles it will encourage the rest of the players to follow.

In the panel discussion one of the questions that Denise McKenzie asked the panellists was “what was your lightbulb moment?” I have been on the edge of conversations about location and privacy for well over a decade now and have gone from a position of predicting a privacy disaster when location history was unexpectedly used as evidence in a legal or commercial dispute to taking a pretty laissez faire view about my own location history and data – it’s almost impossible to live in the digital world without dropping location breadcrumbs for businesses to hoover up!

But the lightbulb moment for me was not about personal privacy, it was when I first grasped the potential for algorithmic bias in geo applications. At FOSS4G 2017 in Boston I was listening to a talk about a Boston city programme to connect young people with internship opportunities during the summer vacations. The programme recruited a number of employers across the city who offered internships and then used a simple geospatial app to match applicants with opportunities while minimising travel distance. That sounded reasonable until you discovered that the opportunities were not uniformly spatially distributed, in fact they were clustered closer to the middle class parts of the city, so optimising on travel distance significantly disadvantaged the young people coming from poorer backgrounds who were meant to be the beneficiaries of the scheme. Simple really but probably a lesson we could all learn when building geospatial applications and covered by principle 6 in the Locus Charter.

Congratulations to Denise McKenzie and Ben Hawes for their work on the charter and respect to Ordnance Survey and the Omidyar Network for funding the Benchmark Initiative. I think this could be a tipping point in our incorporation of geo-ethics into our businesses and practices.
24 March 2021
11:50
geomati.co: Visualización transporte público en tiempo real
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Transportes Metropolitanos de Barcelona en tiempo real

La entrada Visualización transporte público en tiempo real se publicó primero en Geomatico.
23 March 2021
10:00
CARTO Blog: CARTO for React: A faster way to develop spatial applications
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Application development is a key requirement for many organizations that need to provide custom experiences for their users. This can range from a simple interface providin...
1:00
EOX' blog: Sentinel-2 cloudless 2020
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Another year went by and our most recent update of the EOxCloudless satellite map Sentinel-2 cloudless has been released! Layer comparison from 2016 to 2020 Apart from major technical improvements of the data foundation the website Sentinel-2 cloudless has got a new coat as well. You can now browse ...
22 March 2021
16:01
GeoTools Team: GeoTools 25.0 released
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The GeoTools team is pleased to share the availability GeoTools 25.0 : geotools-25.0-bin.zip geotools-25.0-doc.zip geotools-25.0-userguide.zip geotools-25.0-project.zip This release is also available from the OSGeo Maven Repository and is made in conjunction with GeoServer 2.19.0 and JTS 1.18.1.Change DataStore Parameters Map<String,Serilizable> to Map<String,?>This API change makes it easier
1:00
GeoServer Team: GeoServer 2.19.0 Released
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We are happy to announce GeoServer 2.19.0 release is available for download (zip and war) along with docs and extensions.

This GeoServer 2.19.0 release was produced in conjunction with GeoTools 25.0 and GeoWebCache 1.19.0.

Thanks to everyone who contributed and helped test this release. Developer adding new features are credited in the sections below. Release candidate testing was performed by Andrea Aime, Bart Verbeeck, Christoforos Vradis, Georg Weickelt, Graham Humphries, Ian Turton, Jody Garnett, Peter Rushforth, Richard Duivenvoorde, Russell Grew, and Simone Giannecchini. With all the new extensions being added we appreciated those testing the release candidate packaging.

Thanks to Alessandro Parma (GeoSolutions) and Andrea Aime (GeoSolutions) for making this release.
MapML extension
In this release, MapML has graduated from community module to extension status. Map Markup Language (MapML) is a proposed extension to HTML, for maps. The objective of the project is to standardize accessible, performant Web maps with native support from Web browsers (maps in HTML). The GeoServer MapML extension will closely track the MapML specification as it evolves. Find out more at [https:] and if you like our goals, join the community group!

The MapML extension works with GeoServer layers and layer groups, and uses WMS, WMTS and WFS facilities built into GeoServer to provide simple map previews layers. The layer previews can be “dragged” from one browser tab onto another map preview to visualize a mashup of the layers of layer groups using the built in MapML viewer.

Editing a layer’s MapML properties in the Layers panel

Editing a layer’s MapML gridsets in the Layers panel Tile Caching tab

Preview a layer in the MapML viewer by following the MapML link

Mash up MapML previews by drag and drop

The Maps for HTML community would like to thank Andrea Aime, Jody Garnett and the GeoServer PSC for their support and help in getting this extension published.

More information on the GeoServer MapML extension is available in the user guide
WPS JDBC extension
The WPS JDBC extension allows to share the status of asynchronous WPS requests across a GeoServer cluster. The status of all requests, past and ongoing, can be stored in a database, for later reference.

The module uses GeoTools JDBC stores to access databases, create the necessary tables, and track status. Connection parameters are provided as property files, e.g.:
```
user=postgres
port=5432
password=******
passwd=******
host=localhost
database=gsstore
driver=org.postgresql.Driver
dbtype=postgis
```
For more information, refer to the module documentation.

We’d like to thank Ian Turton for developing the module on behalf of GeoSolutions, Alessio Fabiani (GeoSolutions) for providing documentation for it, and Andrea Aime (GeoSolutions) for performing the QA and graduation steps.
WPS Download extension
The WPS download plugin provides support for the download of large amounts of data, allowing use of asynchronous requests, where using WFS, WCS or WMS for the same task would lead to HTTP timeouts. Also, download limits can be configured to avoid excessively large requests: size in MB, number of features, number of animation frames.

In particular, the following processes are available:
- DownloadEstimator, verifying that a raster/vector download about to be attempted will fit the download limits.
- DownloadProcess, allowing to download either raster or vector data, reproject and clip them
- DownloadMapProcess, allows to download a large map matching what is visible on a client (which may be using tiles and display on a multi-screen), eventually dynamically fetching layers from remote WMS servers as well. It’s also possible to decorate the final map using the standard decoration layouts.
- DownloadAnimationProcess, allows to build a MP4 movie given a set of layers and times.
GeoNode uses the module to allow download of datasets, eventually clipped and filtered to the current view. The asynchronous download allows to download large datasets, and retrieve them later, once ready.

Initiating a download in GeoNode

Configuring the download

The download package is ready

Viewing the clipped download in QGIS

For more information, refer to the module documentation.

Thanks to Alessio, Andrea, Daniele, from GeoSolutions, for developing the extension, and GeoNode/MapStore for testing it in various production environments.
WMTS Multidimensional extension
The WMTS multi-dimensional extension is an extension to the WMTS protocol developed during OGC Testbed 12. The extension allows to explore the dimensions attached to a dataset, providing ways to explore them, finding relationships between them.

Here are a couple of real world examples of this functionality:
- GeoServer is publishing a set of satellite images. Each image is time stamped. The user is browsing the set of data on a map, and the client software wants to show the list of available times for the current area. The WMS/WMTS dimension support cannot help, but the WMTS extension has a request, GetDomainValues, which exactly answers this question.
- GeoServer is publishing a set of NetCDFs containing weather forecasts. Each dataset has two times associated, a run time (the time the forecast was run) and a time (the predicted time for the weather data). Forecasts are run for the short term future, so the two times are strictly related. A user wants to compare forecasts for a given predicted time. The GetDomainValues request can be used to locate the run times that have a prediction for the given forecast time.
- GeoServer is publishing a set of timestamped data. The client wants to display a timeline, providing an idea of which times are available for the current view. In addition to that, the clients wants to display how many datasets are available along the timeline. The GetHistogram request can be used to retrieve a count of datasets available over time buckets in a given interval.
The MapStore client uses the module to power its timeline extension, providing time discovery, navigation, animation, and histogram display.

MapStore timeline plugin, with animation controls

MapStore timeline plugin, histogram view

The plugin partially replaces the WMS animator functionality, which is going to be deprecated (since it’s memory bound, and can only be accessed with a synchronous request).

For more information, refer to the module documentation.

Thanks to Nuno Oliveira (GeoSolutions) and Andrea Aime (GeoSolutions) for the initial development, and MapStore for adopting the module, using it in production, and ensuring its long term development
Params-extractor extension
The parameter extractor module is used to inject vendor parameters in all links that a standard OGC client uses, by either reflecting them into the Capabilities documents backlinks, or hiding them in an extra component in the URLs paths.

This can be used, for example, to provide a desktop client, such as QGIS, a different view of a given layer based on viewparams, cql_filter or env parameters, even if the client would not be able to use the parameters natively. Each combination of parameters receives a different starting GetCapabilities request.

A simple query parameter echoing can be setup for clients honoring query parameters in capabilities backlinks:

Parameter extractor echoing

For clients ignoring query parameters or even ignoring backlinks, the parameters can be added as a path component instead, and then expanded in a larger templated value:

Parameter expansion from path component

With the above setup, a URL ending with H11:

/geoserver/tiger/wms/H11?SERVICE=WMS&VERSION=1.1.1&REQUEST=GetMap

is interpreted as:

/geoserver/tiger/wms?SERVICE=WMS&VERSION=1.1.1&REQUEST=GetMap&CQL_FILTER=CFCC%3D%27H11%27

For more information, refer to the module documentation.

Thanks to Nuno Oliveira (GeoSolutions) for developing this module.
GeoWebCache-S3 extension
The GeoWebCache S3 blobstore allows storage of GeoWebCache tiles in a S3 bucket. It has been also tested with a few other S3 compatible blob storage mechanisms, such as Minio.

This plugin is particularly useful when deploying GeoServer on AWS, but also when setting up a shared tile storage in Kubernetes.

Setting up the S3 tile storage

For more information, refer to the module documentation.
Retire ArcSDE Extensions
The ArcSDE Extension has been retired.

In this case we found that the extension is no longer actively used, and lacked sufficient feedback and resources for continued development. The last tested ArcSDE 10.2.2 version is no longer available, making the required jars required for installation unavailable.
Retire the Script community module
The Script community module has been retired.

The module provided scripting abilities for GeoServer, allowing to add WPS processes and small REST services in scripting languages, and storing them in the data directory.

Unfortunately the module fell un-maintained and would no longer build nor work.
JTS 1.18.1
GeoServer 2.19.0 includes the latest JTS Topology Suite 1.18.1 release, the headline feature is an optional “Overlay Next Generation” implementation that should provide a performance improvement for operations such as tile generation, vector tiles, and get map requests.

To try it out use the system property -Djts.overlay=ng - the effect should be small as we already have several optimizations in place before trying this now faster JTS Overlay.

Thanks to Martin Davis (Crunch Data) and James Hughes (CCRi) for making JTS 1.18.1 available during our release window.
Codebase updates and Quality Assurance
GeoServer continues to be build with the latest open source technologies:
- GeoTools 25.0
- GeoWebCache 1.19.0
- JTS 1.18.1
- JAI-EXT 1.1.19
- GeoFence 3.4.7
- Upgrade oshi-core from 5.4.0 to 5.5.0 for new Apple hardware support
- Freemarker 2.3.31
We do not get a chance to talk about the code-base that makes up GeoServer often, but recent changes and improvements deserve some praise. The GeoServer team has really embraced automating code checks, starting with simply formatting the code in a consistent fashion, to more advanced techniques checking for common mistakes.
- Switch most of the unit tests from JUnit 3 to JUnit 4
- Remove usage of Vector/Hashtable, replace with ArrayList and HashMap, add PMD rule to enforce it
- Remove un-necessary casts from code, add PMD rule to enforce it
- Replace try/finally with try-with-resources, add a PMD rule to enforce it
- Collapse catch statements with the same body in a multi-catch, add PMD rule to enforce it
- Avoid assertTrue for tests that can be expressed with dedicated assertions. Add PMD rule to enforce it.
- Replace iterator loops with enhanced for loops, add a QA rule to enforce it.
- Run PMD checks on test sources as well.
- Use Collection.isEmpty() when checking for item availability
- Remove explicit types when diamond operator can be used instead. Added a PMD rule to enforce it.
- Remove or suppress unchecked casts, enable the Java compiler lint option for it.
Although all these changes sound small in isolation, the fact that they are performed on the entire codebase, and checked each time a pull-request is proposed, really provides confidence in the technology we publish.

Thanks to Andrea for this valuable work.
And more!
There are several other new features and improvements, including:
- Upgrade SQL Server packaging to use open source JDBC driver
- Setting Entity Expansion limit on WFS XML Readers
- Tutorial on running GeoServer in cloud foundry.
- Updated DB2 installation instructions
Find out more in the release notes.
About GeoServer 2.19
Additional information on GeoServer 2.19 series:
17 March 2021
14:44
Free and Open Source GIS Ramblings: Movement data in GIS #34: a protocol for exploring movement data
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After writing “Towards a template for exploring movement data” last year, I spent a lot of time thinking about how to develop a solid approach for movement data exploration that would help analysts and scientists to better understand their datasets. Finally, my search led me to the excellent paper “A protocol for data exploration to avoid common statistical problems” by Zuur et al. (2010). What they had done for the analysis of common ecological datasets was very close to what I was trying to achieve for movement data. I followed Zuur et al.’s approach of a exploratory data analysis (EDA) protocol and combined it with a typology of movement data quality problems building on Andrienko et al. (2016). Finally, I brought it all together in a Jupyter notebook implementation which you can now find on Github.

There are two options for running the notebook:
1. The repo contains a Dockerfile you can use to spin up a container including all necessary datasets and a fitting Python environment.
2. Alternatively, you can download the datasets manually and set up the Python environment using the provided environment.yml file.
The dataset contains over 10 million location records. Most visualizations are based on Holoviz Datashader with a sprinkling of MovingPandas for visualizing individual trajectories.

Point density map of 10 million location records, visualized using Datashader

Line density map for detecting gaps in tracks, visualized using Datashader

Example trajectory with strong jitter, visualized using MovingPandas & GeoViews

I hope this reference implementation will provide a starting point for many others who are working with movement data and who want to structure their data exploration workflow.

If you want to dive deeper, here’s the paper:

[1] Graser, A. (2021). An exploratory data analysis protocol for identifying problems in continuous movement data. Journal of Location Based Services. doi:10.1080/17489725.2021.1900612.

(If you don’t have institutional access to the journal, the publisher provides 50 free copies using this link. Once those are used up, just leave a comment below and I can email you a copy.)

References
- Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in ecology and evolution, 1(1), 3-14. [https:]
- Andrienko, G., Andrienko, N., & Fuchs, G. (2016). Understanding movement data quality. Journal of location Based services, 10(1), 31-46. doi:10.1080/17489725.2016.1169322.
This post is part of a series. Read more about movement data in GIS.
10:00
CARTO Blog: Interactive School District Maps & Analysis with WXY
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Recently WXY, an architecture, urban design, and planning firm, was tasked with analyzing 200+ school boundaries within Montgomery County Public Schools (MCPS). As a part o...
16 March 2021
12:27
Blog 2 Engenheiros: Como criar tabelas no layout de impressão usando HTML no QGIS?
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Existem várias situações em estudos ambientais que desejamos mostrar uma tabela no mapa que criamos. Podemos querer apresentar as áreas dos usos do solo do local estudado, mostrar os vértices da poligonal de intervenção ou ainda criar um selo para o nosso mapa.

Para criar uma tabela no layout de impressão do QGIS, temos duas possibilidades. A primeira é utilizar o comando tradicional do QGIS, disponível em Adicionar Item > Adicionar Tabela de Atributos. A segunda é a criação de tabelas por meio de HTML.

No QGIS 3.16, há ainda a opção de Adicionar Tabela Fixa, onde você consegue inserir os seus dados manualmente, como se fosse um editor de planilhas.

Há diferentes situações em que você irá preferir a primeira ou a segunda. Gosto da segunda (HTML) pois consigo editar os valores da tabela no bloco de notas e facilmente atualizar a tabela no QGIS. Além disso, a aparência dela me agrada mais e posso mesclar células nela.

Confira nosso curso de Geoprocessamento para Estudos Ambientais usando QGIS.

Neste tutorial, vamos mostrar como criar uma tabela em HTML contendo os vértices de uma área fictícia que abrange o estado de Santa Catarina.
Criando nossa área de estudo
Criamos um grande polígono que abrange o estado catarinense inteiro. Para extrairmos os seus vértices, vamos utilizar a ferramenta Extrair Vértices, disponível em Vetor > Geometrias. Usaremos nossa área de estudo como dado de entrada. Ao final desse processo, você deverá ter algo como a figura abaixo.
Polígono criado e seus respectivos vértices.
Agora, vamos abrir a tabela de atributos dos vértices e criar duas novas colunas, uma delas contendo a latitude e outra a longitude.

Para criar essas colunas, na tabela de atributos, procure por ‘Abrir Calculadora de Campo’. Na nova janela, marque a opção ‘Criar novo campo’ (1), defina o nome da coluna e suas propriedades (2) e digite o código para obter as coordenadas (3), onde $x é usado para a longitude e $y para a latitude.
Obtendo coordenadas na calculadora de campo.
Agora que temos nossos dados em mãos, vamos à configuração do nosso layout de impressão.
Adicionando tabela de atributos no layout de impressão
Conforme já colocamos anteriormente, a adição de uma tabela de atributos no QGIS é bem simples e pode ser realizada pelo seguinte caminho, no layout de impressão, Adicionar Item > Adicionar Tabela de Atributos.

Após adicionar a tabela, você terá que selecionar o shapefile que terá sua tabela de atributos disposta (1). Em seguida, você poderá selecionar quais colunas irão aparecer (2) e configurar as propriedades visuais da tabela (3).
Adicionando tabela de atributos no layout de impressão.
Agora, caso a aparência dessa tabela não fique do seu agrado, podemos trabalhar com HTML. Veja a seguir.
Criando uma tabela HTML
Você criar sua tabela usando o bloco de notas, um programa específico para HTML ou algum serviço online. No nosso tutorial, vamos utilizar o site TableGenerator.com ( [https:]] ).

Neste site, você poderá adicionar os itens da sua tabela, configurar sua cor e tamanho e depois gerar o HTML clicando em ‘Generate’. Ao final do processo, você terá algo como a imagem abaixo.
Gerando tabelas online.
Após clicar em ‘Generate’, copie o código gerado para usamos no QGIS.
Adicionando HTML no layout de impressão
Temos duas opções para carregar nosso código HTML no QGIS.

Você pode copiar e colar o código em um bloco de notas e salvá-lo como HTML ou copiar e colar o código diretamente no QGIS. Vamos fazer esse segundo.

No layout de impressão, vá em Adicionar Item > Adicionar HTML. Demarque o local onde a tabela vai ficar e depois vamos editar as propriedades deste item.

Nas propriedades do item, caso você tenha salvo o HTML em um arquivo separado, use a opção ‘URL’. No nosso caso, vamos selecionar ‘fonte’ e colar o código na janela habilitada e depois clique em ‘Atualizar HTML’.

A tabela gerada (e as propriedades dela) podem ser visualizadas na figura abaixo.
Adicionando tabelas de HTML no QGIS.
Caso você queira alterar alguma informação da tabela, você pode mudar diretamente no código HTML. Veja alguns exemplos abaixo:
- border-color:black = Este item representa a cor da borda da tabela, atualmente, a cor selecionada é preta (black);
- border-style:solid = Estilo da linha das bordas, neste caso, é uma linha sólida;
- border-width:1px = Espessura da linha da borda em pixels;
- font-family:Arial, sans-serif = Família de fontes a serem usadas para mostrar as palavras;
- font-size:14px = Tamanho da fonte em pixels;
- padding:10px 5px = Espaçamento vertical (10 px) e inicial/horizontal (5 px);
- background-color:#ffffff = Cor de fundo da célula;
- text-align:center = Alinhamento horizontal de texto;
- vertical-align:middle = Alinhamento vertical do texto.
Você irá notar que algumas dessas informações se repetem, isso acontece pois o código separa a configuração das células de cabeçario (th) das células comuns (td).

Realizando algumas modificações aleatórias, temos algo como a figura abaixo.
Modificações aleatórias realizadas na nossa tabela inicial.
E com isso, finalizamos nosso tutorial sobre tabelas de atributos. Agora você já sabe como criá-las de duas formas. Caso você ainda tenha alguma dúvida, utilize os comentários para falar conosco.

Confira nosso curso de Geoprocessamento para Estudos Ambientais usando QGIS.
The post Como criar tabelas no layout de impressão usando HTML no QGIS? first appeared on Blog 2 Engenheiros.
10:00
CARTO Blog: Agricultural Sustainability with CARTO, Indigo Ag, & Snowflake
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Indigo Ag works every day to help farmers harness nature to sustainably feed the planet. Through their Grain and Transport Marketplaces and Carbon program, Indigo facilitat...
7:20
gvSIG Team: Jornadas “Uso de las Tecnologías Libres de Información Geográficas en Educación Básica – experiencias iberoamericanas”
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La Red GeoLIBERO invita a participar de las Jornadas denominadas Uso de las Tecnologías Libres de Información Geográficas en Educación Básica – experiencias iberoamericanas. Las jornadas se desarrollarán en 2 días: 12 y 13 de mayo de 2021. Habrá un día para presentar los distintos casos de éxito de los 3 años de experiencia del Curso-Concurso Proyectos con estudiantes y gvSIG Batoví, y otro para presentaciones de trabajos iberoamericanos, para lo cual los invitamos a participar, presentando comunicaciones referidas al uso de Tecnologías Libres de Información Geográfica en Educación Básica, con el fin de dar a conocer experiencias, investigaciones, proyectos y acciones llevadas a cabo, en curso o planificadas, en la región iberoamericana.

Los objetivos de las Jornadas son:
- conocer experiencias iberoamericanas de uso de las Tecnologías Libres de Información Geográficas en Educación Básica
- intercambiar conocimientos y aprendizajes entre la comunidad iberoamericana acerca del uso de estas tecnologías como herramientas de enseñanza pre-universitaria
- presentar las experiencias en Uruguay de la iniciativa Curso-Concurso Proyectos de trabajo con estudiantes y gvSIG Batoví
- potenciar sinergias entre los participantes que permitan el desarrollo de proyectos colaborartivos a futuro
- ser un insumo invalorable para la Linea de Investigación en Educaciòn del proyecto GEOLibero, que patrocina el evento
15 March 2021
10:00
CARTO Blog: #SDSC21: Financial Services Summit Agenda Now Available
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The Spatial Data Science in Financial Services Summit, a part of #SDSC21, is just over a week away and we are very excited to announce the full speaker lineup, bringing tog...
11 March 2021
13:02
Free and Open Source GIS Ramblings: Movement data in GIS #33: “Exploratory analysis of massive movement data” webinar
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Yesterday, I had the pleasure to speak at the RGS-IBG GIScience Research Group seminar. The talk presents methods for the exploration of movement patterns in massive quasi-continuous GPS tracking datasets containing billions of records using distributed computing approaches.

Here’s the full recording of my talk and follow-up discussion:

and slides are available as well.

This post is part of a series. Read more about movement data in GIS.
7:00
Lutra consulting: Project wizard and packaging for Mergin in QGIS
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Based on the feedback from users, it appeared that packaging and creating a stand-alone project for using on the Input app were sometimes cumbersome and confusing. Existing projects have links to several layers and those layers can be spread over different network drives or folders.

With the new release of the Mergin plugin for QGIS, we have introduced a project wizard, which can help users to package and upload the project to the Mergin service. There is also a new toolbar to simplify your workflow.
Easy access to Mergin tools
The plugin now comes with a new toolbar. The common tasks (syncing the project/data and checking the status of the project) are now available from the toolbar.
Project wizard
To create a new project, you can start from a blank project or alternatively open an existing project. You can then select Create Mergin Project from the toolbar:

A new window will appear which should give you three options (the last two options are available only if you have an existing project open):
- New basic QGIS project: if you are new to QGIS, this is a good starting point. With this option, a project will be created. Within the project there will be a survey layer (a point layer) and background map (OpenStreetMap).
- Package current QGIS project: this option will create a copy of your project and copies all the files to a single folder. The wizard tries to guess each format and offers users three options to package the layer, keep as is (i.e. the layer will be referenced as is in the new project) or ignore (the layer will not be included in the new project). The default for each layer type is as: the web services (e.g. WMTS, XYZ tiles, vector tile layers) will be referenced in the new project as they are. Vector layers will be all written to Geopackage format (each vector in one Geopackage database). Raster layers will be copied as they are. The layers will be referenced in the new project accordingly.
- Use current QGIS project as is: this is for cases when you have already a stand-alone folder with your projects packaged.
In the next window, you will be prompted to assign a project name and select a path where your project folder and associated files will be generated (this option is only available for the first two choices):

After the wizard, the new project will be created locally and on the Mergin server.
Validation and status check
It is recommended to run the project status after changing your layers and project. This will help getting a list of pending changes and also see any warnings or validations of your project. The warnings are related to restructuring of a Geopackage layer (adding/removing a field or addding/removing a layer in a Geopackage database). Validations can be linked to missing layer or availability of a layer when working offline:

Reporting issues
If you have any issues or suggestion to improve the plugin, you can file a ticket on the Github project repository.
What next
Your project is ready to be used in Input. Get the app for your Android or iOS device. Log into the Mergin service and download the project(s) created in the above section on your device.
06 March 2021
20:59
From GIS to Remote Sensing: Major Update: Semi-Automatic Classification Plugin v. 7.8.0
sur Planet OSGeo

This post is about a new update of the Semi-Automatic Classification Plugin v. 7.8.0.
Following the changelog:-new tool for analysis of neighbor pixel with convolution matrix-improved performance with multiprocessing calculations-fixed conversion to vector

This new version includes a new Preprocessing tool named Neighbor pixels.This tool allows for the calculation of several neighbor pixel statistics for every band of a band set defined in the Band set.

The statistics are calculated for every pixel of the input raster considering the values of the neighbor pixels. Neighbor pixels are defined through a distance or through a custom matrix.For example, the following matrix represents the neighbor pixels within a distance of 1 pixel from a central pixel, resulting in a 3x3 matrix.
Neighbor Neighbor Neighbor
Neighbor Center Neighbor
Neighbor Neighbor Neighbor

Several statistics are available. The statistic Sum will result in a raster convolution. For instance, this can be useful to apply an image filter to all the bands a band set for photointerpretation.An output band is created for every band in the band sets.
Also, the multiprocessing calculations have been optimized and calculations should generally be more rapid compared to previous version.
For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin.
04 March 2021
21:00
GeoTools Team: GeoTools 25-RC Release Candidate
sur Planet OSGeo

The GeoTools team is pleased to share the availability GeoTools 25-RC : geotools-25-RC-bin.zip geotools-25-RC-doc.zip geotools-25-RC-userguide.zip geotools-25-RC-project.zip This release candidate is also available from the OSGeo Maven Repository and is made in conjunction with GeoServer 2.19-RC and JTS 1.18.1.Please Test this Release Candidate A release candidate is your chance to both try out
10:00
CARTO Blog: How Google Cloud & CARTO power geospatial analysis at scale
sur Planet OSGeo

As the volume of data with a location component continues to grow exponentially, both geospatial analysts and data scientists are looking to scale their analytical workflow...
4:17
GeoTools Team: GeoTools default branch changed to main
sur Planet OSGeo

A quick public service announcement, the GeoTools default branch has changed to main. To update your local repository: git branch -m master maingit fetch upstreamgit branch -u upstream/main main GeoTools is following the development ecosystem (git, gitlab, github, bitbucket) and information technology industry effort to replace the use of word " master " in a
1:00
GeoServer Team: GeoServer repository transition to main branch
sur Planet OSGeo
The GeoServer project is changing our default branch to main.

The casual use of the words “master” and “slave” in computer software is an unnecessary reference to a painful human experience that continue to impact society.

The change is part of an industry shift made possible by the git, bitbucket, github and gitlab projects. The git command line, repository implementations, now support main as default branch setting.

To update your local repository:
```
git branch -m master main
git fetch upstream
git branch -u upstream/main main
```
To configure your local git tool so that new repositories are created with a main branch:
```
git config --global init.defaultBranch main
```
1:00
GeoServer Team: GeoServer 2.19-RC Released
sur Planet OSGeo
We are happy to announce GeoServer 2.19-RC release candidate is available for testing. Downloads are available (zip and war) along with docs and extensions.

This is a GeoServer release candidate made in conjunction with GeoTools 25-RC and GeoWebCache 1.19-RC.
- Release candidates are a community building exercise and are not intended for production use.
- We ask the community (everyone: individuals, organizations, service providers) to download and thoroughly test this release candidate and report back.
- Participating in testing release candidates is a key expectation of our open source social contract. We make an effort to thank each person who tests in our release announcement and project presentations!
- GeoServer commercial service providers are fully expected to test on behalf of their customers.
Release Candidate Testing Priorities
This is an exciting release and a lot of great new functionality has been added. We would like to ask for your assistance testing the following:
- The number one testing priority is to try out GeoServer with your data! Mass market open source thrives on having many people to review. Scientific open source like GeoServer thrives on exposure to many datasets.
- Help check that new extension download bundles have contain everything needed, including appropriate readme instructions and open source license information.
- The rest of this blog post highlights new features for GeoServer 2.19, please try out these features, read the documentation links, and ask questions.
Known Issues:
- Layer configured with missing style throws NPE
MapML extension
In this release, MapML has graduated from community module to extension status. Map Markup Language (MapML) is a proposed extension to HTML, for maps. The objective of the project is to standardize accessible, performant Web maps with native support from Web browsers (maps in HTML). The GeoServer MapML extension will closely track the MapML specification as it evolves. Find out more at [https:] and if you like our goals, join the community group!

The MapML extension works with GeoServer layers and layer groups, and uses WMS, WMTS and WFS facilities built into GeoServer to provide simple map previews layers. The layer previews can be “dragged” from one browser tab onto another map preview to visualize a mashup of the layers of layer groups using the built in MapML viewer.

Editing a layer’s MapML properties in the Layers panel

Editing a layer’s MapML gridsets in the Layers panel Tile Caching tab

Preview a layer in the MapML viewer by following the MapML link

Mash up MapML previews by drag and drop

The Maps for HTML community would like to thank Andrea Aime, Jody Garnett and the GeoServer PSC for their support and help in getting this extension published.

More information on the GeoServer MapML extension is available in the user guide
WPS JDBC extension
The WPS JDBC extension allows to share the status of asynchronous WPS requests across a GeoServer cluster. The status of all requests, past and ongoing, can be stored in a database, for later reference.

The module uses GeoTools JDBC stores to access databases, create the necessary tables, and track status. Connection parameters are provided as property files, e.g.:
```
user=postgres
port=5432
password=******
passwd=******
host=localhost
database=gsstore
driver=org.postgresql.Driver
dbtype=postgis
```
For more information, refer to the module documentation.

We’d like to thank Ian Turton for developing the module on behalf of GeoSolutions, Alessio Fabiani (GeoSolutions) for providing documentation for it, and Andrea Aime (GeoSolutions) for performing the QA and graduation steps.
WPS Download extension
The WPS download plugin provides support for the download of large amounts of data, allowing use of asynchronous requests, where using WFS, WCS or WMS for the same task would lead to HTTP timeouts. Also, download limits can be configured to avoid excessively large requests: size in MB, number of features, number of animation frames.

In particular, the following processes are available:
- DownloadEstimator, verifying that a raster/vector download about to be attempted will fit the download limits.
- DownloadProcess, allowing to download either raster or vector data, reproject and clip them
- DownloadMapProcess, allows to download a large map matching what is visible on a client (which may be using tiles and display on a multi-screen), eventually dynamically fetching layers from remote WMS servers as well. It’s also possible to decorate the final map using the standard decoration layouts.
- DownloadAnimationProcess, allows to build a MP4 movie given a set of layers and times.
GeoNode uses the module to allow download of datasets, eventually clipped and filtered to the current view. The asynchronous download allows to download large datasets, and retrieve them later, once ready.

Initiating a download in GeoNode

Configuring the download

The download package is ready

Viewing the clipped download in QGIS

For more information, refer to the module documentation.

Thanks to Alessio, Andrea, Daniele, from GeoSolutions, for developing the extension, and GeoNode/MapStore for testing it in various production environments.
WMTS Multidimensional extension
The WMTS multi-dimensional extension is an extension to the WMTS protocol developed during OGC Testbed 12. The extension allows to explore the dimensions attached to a dataset, providing ways to explore them, finding relationships between them.

Here are a couple of real world examples of this functionality:
- GeoServer is publishing a set of satellite images. Each image is time stamped. The user is browsing the set of data on a map, and the client software wants to show the list of available times for the current area. The WMS/WMTS dimension support cannot help, but the WMTS extension has a request, GetDomainValues, which exactly answers this question.
- GeoServer is publishing a set of NetCDFs containing weather forecasts. Each dataset has two times associated, a run time (the time the forecast was run) and a time (the predicted time for the weather data). Forecasts are run for the short term future, so the two times are strictly related. A user wants to compare forecasts for a given predicted time. The GetDomainValues request can be used to locate the run times that have a prediction for the given forecast time.
- GeoServer is publishing a set of timestamped data. The client wants to display a timeline, providing an idea of which times are available for the current view. In addition to that, the clients wants to display how many datasets are available along the timeline. The GetHistogram request can be used to retrieve a count of datasets available over time buckets in a given interval.
The MapStore client uses the module to power its timeline extension, providing time discovery, navigation, animation, and histogram display.

MapStore timeline plugin, with animation controls

MapStore timeline plugin, histogram view

For more information, refer to the module documentation.

Thanks to Nuno Oliveira (GeoSolutions) and Andrea Aime (GeoSolutions) for the initial development, and MapStore for adopting the module, using it in production, and ensuring its long term development
Params-extractor extension
The parameter extractor module is used to inject vendor parameters in all links that a standard OGC client uses, by either reflecting them into the Capabilities documents backlinks, or hiding them in an extra component in the URLs paths.

This can be used, for example, to provide a desktop client, such as QGIS, a different view of a given layer based on viewparams, cql_filter or env parameters, even if the client would not be able to use the parameters natively. Each combination of parameters receives a different starting GetCapabilities request.

A simple query parameter echoing can be setup for clients honoring query parameters in capabilities backlinks:

Parameter extractor echoing

For clients ignoring query parameters or even ignoring backlinks, the parameters can be added as a path component instead, and then expanded in a larger templated value:

Parameter expansion from path component

With the above setup, a URL ending with H11:

/geoserver/tiger/wms/H11?SERVICE=WMS&VERSION=1.1.1&REQUEST=GetMap

is interpreted as:

/geoserver/tiger/wms?SERVICE=WMS&VERSION=1.1.1&REQUEST=GetMap&CQL_FILTER=CFCC%3D%27H11%27

For more information, refer to the module documentation.

Thanks to Nuno Oliveira (GeoSolutions) for developing this module.
GeoWebCache-S3 extension
The GeoWebCache S3 blobstore allows to store GeoWebCache tiles in a S3 bucket. It has been also tested with a few other S3 compatible blob storage mechanisms, such as Minio.

This plugin is particularly useful when deploying GeoServer on AWS, but also when setting up a shared tile storage in Kubernetes.

Setting up the S3 tile storage

For more information, refer to the module documentation.
Retire ArcSDE Extensions
The ArcSDE Extension has been retired.

In this case we found that the extension is no longer actively used, and lacked sufficient feedback and resources for continued development. The last tested ArcSDE 10.2.2 version is no longer available, making the required jars required for installation unavailable.
Retire the Script community module
The Script community module has been retired.

The module provided scripting abilities for GeoServer, allowing to add WPS processes and small REST services in scripting languages, and storing them in the data directory.

Unfortunately the module fell un-maintained and would no longer build nor work.
Codebase updates and Quality Assurance
GeoServer continues to be build with the latest open source technologies:
- GeoTools 25-RC
- GeoWebCache 1.19-RC
- JAI-EXT 1.1.19
- JTS 1.18.1
- GeoFence 3.4.7
- Upgrade oshi-core from 5.4.0 to 5.5.0 for new Apple hardware support
- Freemarker 2.3.31
We do not get a chance to talk about the code-base that makes up GeoServer often, but recent changes and improvements deserve some praise. The GeoServer team has really embraced automating code checks, starting with simply formatting the code in a consistent fashion, to more advanced techniques checking for common mistakes.
- Switch most of the unit tests from JUnit 3 to JUnit 4
- Remove usage of Vector/Hashtable, replace with ArrayList and HashMap, add PMD rule to enforce it
- Remove un-necessary casts from code, add PMD rule to enforce it
- Replace try/finally with try-with-resources, add a PMD rule to enforce it
- Collapse catch statements with the same body in a multi-catch, add PMD rule to enforce it
- Avoid assertTrue for tests that can be expressed with dedicated assertions. Add PMD rule to enforce it.
- Replace iterator loops with enhanced for loops, add a QA rule to enforce it.
- Run PMD checks on test sources as well.
- Use Collection.isEmpty() when checking for item availability
- Remove explicit types when diamond operator can be used instead. Added a PMD rule to enforce it.
- Remove or suppress unchecked casts, enable the Java compiler lint option for it.
Although all these changes sound small in isolation, the fact that they are performed on the entire codebase, and checked each time a pull-request is proposed, really provides confidence in the technology we publish.

Thanks to Andrea for this valuable work.
And more!
There are several other new features and improvements, including:
- Upgrade SQL Server packaging to use open source JDBC driver
- Setting Entity Expansion limit on WFS XML Readers
- Tutorial on running GeoServer in cloud foundry.
- Updated DB2 installation instructions
Find out more in the release notes.
About GeoServer 2.19
Additional information on GeoServer 2.19 series:
- GeoServer repository transition to main branch
- Release notes (2.19-RC)
1:00
GeoServer Team: GeoServer repository transition to main branch
sur Planet OSGeo
The GeoServer project is changing our default branch to main.

The casual use of the words “master” and “slave” in computer software is an unnecessary reference to a painful human experience that continue to impact society.

The change is part of an industry shift made possible by the git, bitbucket, github and gitlab projects. The git command line, repository implementations, now support main as default branch setting.

To update your local repository:
```
git branch -m master main
git fetch upstream
git branch -u upstream/main main
```
To configure your local git tool so that new repositories are created with a main branch:
```
git config --global init.defaultBranch main
```
02 March 2021
10:00
CARTO Blog: COVID Vaccine: Mapping Rollout & Optimizing Supply Chain
sur Planet OSGeo

With over 224 million doses of COVID vaccine having been administered to date worldwide, this week also sees the first shipments of the COVID-19 Vaccines Global Access (COV...
01 March 2021
10:00
CARTO Blog: How to Enrich POS Data to Analyze & Predict CPG Sales
sur Planet OSGeo

Over the past year consumer behavior has changed significantly and many believe permanently. Last year U.S. CPG sales rose by 10.3% to $933 billion as consumers stocked up ...
28 February 2021
9:06
Stefano Costa: I libri che ho letto nel 2020
sur Planet OSGeo

Anche quest’anno niente classifiche e niente recensioni ? solo qualche commento. Lo sapete tutti, il 2020 è stato segnato dalla pandemia e come tanti ho passato molto più tempo senza poter uscire. Ho letto un po’ più dello scorso anno, per quel che conta la quantità.
Michele Ainis, Demofollia
Una raccolta di saggi, complessivamente un po’ ripetitiva ma visto che uno dei temi ricorrenti è quello della burocrazia è anche una lettura doverosa. Nel libro e anche nell’introduzione Ainis porta l’esempio paradossale del Ministero dei Beni Culturali che ha cambiato nome tre volte in pochi anni. Ebbene, dopo la pubblicazione del libro è cambiato di nuovo. E all’inizio del 2021 è cambiato di nuovo. Cinque volte.
Matteo Vinzoni, Pianta delle due riviere della serenissima Repubblica di Genova divise ne’ commissariati di sanità (a cura di Massimo Quaini)
Questa è una lettura, sì. Come molti ho consultato decine di volte la Pianta e i suoi disegni, ma non sapevo che fosse accompagnata da un testo descrittivo molto articolato. L’edizione maestra curata dal grande Massimo Quaini ha una lunga introduzione al testo che racconta la storia di Matteo Vinzoni, di come è diventato uno dei più grandi cartografi della sua epoca e della fatica interminabile che gli è costata questa Pianta. E la cosa che più mi ha sorpreso è stata la causa scatenante per l’istituzione dei commissariati di sanità: un’epidemia di peste (cosiddetta peste di Marsiglia, 1720), in cui la cartografia è uno degli strumenti di controllo capillare del territorio al servizio del governo. Quante cose ci sono da imparare.
Margaret Elphinstone, La notte del raduno
Lo scorso anno con “L’ultima dei Neandertal” avevo detto, ci vuole più narrativa ambientata nella preistoria! Eccomi servito. Siamo nel Mesolitico delle isole britanniche e questo racconto a più voci è la storia di un mondo lontanissimo, in cui uomini e donne vivono secondo regole e credenze molto legate alla natura e ai suoi cicli, ma sono anche pieni di grandezza, di spazi immensi, di legami profondi tra persone. L’ho trovato un racconto senza un genere preciso e forse per questo veramente profondo.
Telmo Pievani, La fine del mondo
Mi ha prestato questo libro mio fratello, senza commento. Conoscendo un poco l’autore, mi aspettavo una trattazione sui temi più critici del riscaldamento globale e disastri annessi. Nulla di tutto ciò, il libro di Pievani è una raccolta di sommari culturali-filosofici del mondo occidentale su vari livelli a cui è stata concepita la fine del mondo. Mi ha fastidiosamente ricordato un brutto libro di Remo Bodei letto anni fa. Nessuno spazio è dato alla storia del pensiero nel mondo indiano o cinese. Compaiono i Maya (scritti però minuscoli, diversamente dagli antichi romani maiuscoli) per “lip service” alla più nota delle teorie pseudostoriche catastrofiste. Non mi è piaciuto. Non era piaciuto nemmeno a Enrico.
Chimamanda Ngozi Adichie, Dovremmo essere tutti femministi
Molto breve e tagliente. Quest’anno non ho letto in modo esclusivo autrici, ma ho comunque continuato ad esplorare fuori dalla mia comfort zone.
Ayòbámi Adébáyò, Resta con me (Stay with me)
E questo è molto fuori. Il racconto è a due voci ma quella maschile sembra in molte parti vivere un livello umano diverso, più elementare, nella lunga tragedia che segna la vita della coppia. È stato straziante, soprattutto con un bimbo di pochi mesi in casa, e scava molto profondamente nelle assurde consuetudini che in tanti luoghi controllano la vita delle coppie che si amano.
Claire Cameron, L’orso
Claire Cameron è l’autrice de “L’ultima dei Neandertal”, facile. Ma questo orso è mi-ci-dia-le. Sarà una banalità, ma la voce narrante adorabile di questa storia inquietante rende il libro un vero concentrato di emozioni forti e fortissime. Di nuovo, c’è una quasi corrispondenza con le età dei miei figli e questo mi ha fatto leggere l’orso in maniera molto più riflessiva ? parafrasando Scaruffi la trama è l’ultima cosa interessante di tutto il libro.
Hisham Matar, Un punto di approdo
Questo è un delizioso regalo per il mio compleanno. Mi ha fatto tornare a Siena e capire quante cose ho perso di Siena negli anni in cui ci ho abitato.
Chinua Achebe, La freccia di Dio
Il terzo volume della trilogia, ammesso che sia davvero una trilogia. È spietato e tragico in modo grandioso.
Donna Haraway, A cyborg manifesto
Ho letto con grande fatica, in lingua originale, questo saggio, di cui non avevo capito bene né l’estensione né la dimensione politico-accademica. Ho anche ascoltato una versione “audiolibro” non molto ascoltabile, ma pur avendo colto alcuni concetti fondamentali che oggi sono diventati estremamente potenti, non sono riuscito a seguire per bene il discorso complessivo. Mi ha lasciato un po’ guardingo l’avvio iniziale sul ruolo “ironico” del cyborg, perché mi sto convincendo profondamente che l’ironia sia molto deleteria. Quindi ho comprato la traduzione italiana, che leggerò prossimamente.
Ursula K. Le Guin, I reietti dell’altro pianeta (Dispossessed: an ambiguos utopia)
Ce l’ho fatta! Sono finalmente riuscito a leggere un romanzo di Le Guin. Che meraviglia. Che incredibile viaggio questo sul pianeta Anarres. Scopro l’acqua calda, ma acqua calda rimane.
James Ellroy, Le strade dell’innocenza (Blood on the moon)
Ho questa trilogia da un paio d’anni, comprata usata da “Nostalgie di carta”, la libreria di via Daste che purtroppo ha chiuso i battenti con la morte del titolare. Il primo libro è un “classico” Ellroy, lontanissimo dalle costruzioni complesse degli anni successivi ma comunque una buona lettura.
Paolo Rumiz, La leggenda dei monti naviganti
Questo me lo ha prestato mio padre. È il libro che mi ha fatto pensare di più e sicuramente quello che più mi ha fatto innervosire, non perché sia brutto (vedi sopra la fine del mondo), ma perché mescola una ricerca approfondita di luoghi, persone e storie con una serie di mostruosità che non riesco a tollerare. I beceri stereotipi (la macchina femmina, qua e là donne bellissime che compaiono unicamente come tali). L’ingenuità di fronte ai lavori nel ventre delle montagne (pure ritrattata nel libro stesso). Le lamentele sull’abbandono che solo uno nato e cresciuto in città può concepire.

Il libro è effettivamente composto di due parti (anche qui, dichiarate in apertura), una di autobiografia lenta riguardante le Alpi, i molti personaggi quasi tutti legati all’alpinismo, all’epica della montagna con le sue gesta eroiche e le sue tragedie. Questa parte è forse abbastanza banale. La seconda parte è un diario di viaggio on the road in cui Rumiz attraversa l’intero Appennino evitando tutte le strade principali. In questa seconda parte lo spirito di ascolto è più profondo e segue solo in parte personaggi “famosi”, ma si perde innumerevoli volte di fronte a scempiaggini linguistiche e toponomastiche, è ossessionato da Annibale (su cui ha scritto un altro libro .. ora sul mio comodino) e qualunque cosa incontri lungo il proprio tragitto diventa paradigmatica di qualcosa. Un altro aspetto che trovo fastidioso è il continuo ricorso a paragoni geografici: una nuova valle in cui arriva non merita mai di essere se stessa, ma per avere valore deve per forza assomigliare a qualche altro posto già visto.

A Rumiz stanno sulle palle i gerani ai balconi e lo scrive svariate volte nel libro. È esterofilo ma solo perché il “vero” spirito della montagna in Italia è soverchiato da altri valori, altrimenti saremmo noi i migliori. Mah.
Ursula K. Le Guin, Il mago
La letteratura fantastica e quella fantascientifica non sono così diverse, sembra di capire leggendo questo primo volume della saga di Terramare. La componente psicologica è quella fondamentale nei rapporti tra personaggi e nelle gesta dei protagonisti, ben più della coerenza nel world building o nel linguaggio, che comunque ci sono e sono potenti e sistematici. Ho passato tutta la lettura a consultare la mappa dell’immenso arcipelago, questo mi ha dato grande soddisfazione. Ora voglio con la giusta calma continuare la lettura della saga.

Sto leggendo Seni e uova di Mieko Kawakami. A proposito di uscire dalla zona di confort.
25 February 2021
12:35
Free and Open Source GIS Ramblings: Video recommendations from FOSDEM 2021
sur Planet OSGeo

The Geospatial Dev Room at FOSDEM 2021 was a great event that (virtually) brought together a very diverse group of geo people.

All talk recordings are now available publicly at: fosdem.org/2021/schedule/track/geospatial

In line with the main themes of this blog, I’d particularly like to highlight the following three talks:

MoveTK: the movement toolkit A library for understanding movement by Aniket Mitra

Telegram Bot For Navigation: A perfect map app for a neighbourhood doesn’t need a map by Ilya Zverev

Spatial data exploration in Jupyter notebooks by yours truly
10:00
CARTO Blog: Foursquare POI & Foot Traffic Data now available in CARTO
sur Planet OSGeo

As we enter into the second year of a COVID-19 era, consumers are continuing to shift how they move through the physical world with the physical location of businesses and ...
23 February 2021
7:39
Blog 2 Engenheiros: Como criar perfis longitudinais de rios usando QGIS e Python?
sur Planet OSGeo
Existem várias formas de descrever, fisicamente, uma bacia hidrográfica. Temos a área da bacia, perímetro, fator de forma, densidade de rios, entre outros. A obteção desses parâmetros depende de dados espaciais, sendo assim, precisamos de um software de geoprocessamento.

Dessa forma, vamos utilizar o QGIS 3.14 para obter o perfil longitudinal de alguns rios da Bacia Hidrográfica do Atlântico Sul, tais como o rio Araranguá, rio Urussanga, rio Tubarão, rio Itajaí-Açu e o rio Itapocu. A partir dos dados obtidos, vamos plotar os resultados usando Python.

É importante comentar que alguns rios citados são formados pela junção das águas de outros rios (como o rio Araranguá, formado pelos rios Itoupava e Mãe Luzia), sendo que na nossa análise escolhemos a nascente mais distante para o desenvolver o perfil longitudinal.

Confira nosso curso de Geoprocessamento para Estudos Ambientais usando QGIS.

Antes de iniciarmos, é interessante comentarmos o que é um perfil longitudinal. Ele é um recorte das elevações existentes ao longo da linha de interesse, isto é, a partir de uma determinada linha (no nosso caso, o curso d’água), vamos amostrar as altitude/cotas existentes.

E para que isso serve?

O perfil longitudinal demonstra o relevo existente ao longo do curso d’água, sendo possível observar as declividades existentes, e consequentemente, inferir informações relacionadas à velocidade de escoamento e possiblidade de erosão ou inundação.

Rãdoane e colaboradores (2002) comentam que a parte mais intrigante dos perfis longitudinais são a sua forma, sendo que usualmente tem a forma concava. Eles ainda citam, baseados no trabalho de Gilbert (1877) que a declividade do perfil longitudinal é inversamente proporcional à vazão. Trabalhos posteriores citados pelos autores colocam que a explicação para a forma do perfil longitudional esta principalmente relacionada à variação da vazão, diâmetro do material do leito e a carga de sedimentos.
Quais dados iremos utilizar?
Iremos obter a hidrografia a partir dos dados disponibilizados pela Agência Nacional de Águas (ANA), os quais podem ser baixados aqui; e os dados de elevação usaremos o MDE do SRTM (1 arc), disponível para download no Earth Explorer do serviço geológico norte americano (USGS).

Após inserir o shapefile dos rios, selecione os rios de interesse e crie um shapefile novo com eles (Clique com botão direito sobre o shape na lista de camadas, vá em ‘exportar’ e ‘salvar feições selecionadas como’).

Em seguida, você deve unir os seguimentos (várias linhas) que compõem o mesmo rio. Você deverá editar o shapefile do rio, selecionar as linhas do rio em questão e em seguida, clicar em ‘Mesclar Feições Selecionadas’.
Botão para mesclar feições no QGIS.
Agora para baixar os MDE, no site do Earth Explorer, você terá que primeiro se cadastrar e logar para baixar as imagens (1). Em seguida, busque pela área de interesse (2) e selecione o tipo de imagem que você deseja (3 e 4). Por fim, vá nos resultados (5) para baixar as imagens disponíveis.
Site do Earth Explorer para download de imagens de satélites.
Para a nossa área de interesse, temos vários MDE. Após baixar e adicionar eles ao QGIS, precisamos unir alguns para que eles envolvam toda a extensão dos nossos rios de interesse.
Como unir MDE (Raster) no QGIS?
Antes de começar a unir arquivos matriciais, tenha em mente que eles vão ficar cada vez mais pesados, sendo necessário cada vez mais processamento. Em algumas situações, é importante utilizar arquivos com resoluções espaciais maiores para ter arquivos que possam ser manuseados.

Você pode unir os raster usando a ferramenta Construir um Raster Virtual (Build Virtual Raster), disponível em Raster > Miscelânea. Nesta ferramenta, você deverá selecionar os rasters que você quer unir e depois selecionar onde irá salvá-lo. Neste caso você não precisará alterar as outras informações.
Construindo Raster Virtuais no QGIS.
Após unir os MDE, vamos utilizar o plugin Profile Tool para obter as cotas ao longo do nosso rio.
Usando o plugin Profile Tool
Para ativar o plugin Profile Tool, vá em Complementos e em seguida em ‘Gerenciar e Instalar Complementos’. Procure pelo nome do plugin e clique em ‘Instalar Complemento’.
Plugin Profile Tool do QGIS.
Após instalar o complemento, ative ele clicando sobre ele na janela principal do QGIS.

Para traçar um perfil a partir de uma linha de um shapefile, você deverá seguir o seguinte procedimento:
- Selecionar o MDE que abrange o rio e clicar em Add Layer;
- Em ‘Options’, trocar o ‘Selection’ para ‘Selected Polyline’;
- Selecionar o shapefile que contém os rios e clicar sobre ele.
Após realizar esse procedimento, você terá algo similar a imagem seguinte.
Seleção de um rio para obtenção das suas cotas.
Poderiamos exportar a imagem resultante do plugin e parar por aqui, mas vamos clicar na aba ‘Table’ e vamos clicar em ‘Copy to Clipboard’ para copiar os dados.

Vamos colar esses dados no bloco de notas. Lembre-se de escrever na primeira linha ‘Distancia’, dar um Tab, e em seguida ‘Cota’ (evitando acentos para não dar problema na importação no Python). Depois salve o arquivo como CSV.
Lista de cotas e distâncias obtidas no Profile Tool.
Agora, vamos trabalhar um pouco com Python.
Perfil Longitudinal usando Python
Para a criação do perfil longitudinal, utilizaremos Pandas e Matplotlib (duas bibliotecas do python).

No código abaixo, iremos carregar essas bibliotecas e em seguida, carregar o nosso arquivo com as distâncias e cotas.
```
import pandas as pd
import matplotlib.pyplot as plt

caminho = 'C:/Users/ferna/Desktop/'
arquivo = 'cotas_B2E.csv'

df = pd.read_table(caminho+arquivo, sep="\t")

df['Distance'] = df['Distance'].sort_values(ascending = True).values

print(df.head())
```
Note que nosso código ira puxar nossa tabela, que contem seus valores separados por uma tabulação (‘\t’) e depois organizará os valores de forma crescente.

Agora vamos plotar os resultados.
```
grafico = df.plot(x = 'Distance', y = 'Cota', legend = False, figsize=(12,4))
grafico.set_xlabel('Comprimento do rio (m)')
grafico.set_ylabel('Cota (m)')
grafico.grid()

plt.savefig(caminho+'graficoCotaRio.png', dpi = 120)
```
No caso, teremos um novo gráfico salvo na pasta especificada em ‘caminho’. A próxima figura mostra o nosso resultado para o rio Itapocu.
Perfil longitudinal ‘bruto’ do rio Itapocu.
Note que em função da resolução espacial do nosso MDE, ou ainda em função de ruídos que possam exister no momento da obteção dele, há imperfeições no nosso perfil longitudinal. Para remover ela, vamos passar uma média móvel e todar nosso gráfico novamente.
```
df['med_mov'] = df['Cota'].rolling(30).mean()

grafico = df.plot(x = 'Distance', y = 'med_mov', legend = False, figsize=(12,4))
grafico.set_xlabel('Comprimento do rio (m)')
grafico.set_ylabel('Cota (m)')
plt.title('Rio Itapocu')
grafico.grid()


plt.savefig(caminho+'graficoCotaRio2.png', dpi = 120)
```
Após rodar esse código, temos o seguinte resultado.
Perfil longitudinal do rio Itapocu.
Conforme sua necessidade, você pode alterar o valor da função ‘rolling’ para obter médias móveis diferentes.

Agora você já sabe criar perfis longitudinais, abaixo você pode conferir outros exemplos que montamos.
Perfis longitudinais de outros rios catarinenses.
Caso você tenha ficado com alguma dúvida, utilize os comentários que estaremos respondendo assim que possível.

Quer aprender a usar o QGIS? Confira nosso curso online de Geoprocessamento para Estudos Ambientais.
```
Fontes consultadas:

Flávio Reis. HidroMundo – Declividade e Perfil Longitudinal de um Rio. Disponívem em:  [www.hidromundo.com.br] . Acesso em 22 ja. 2021.

RÃDOANE. M.; RÃDOANE, N.; DUMITRIU, D. Geomorphological evolution of longitudinal river profiles in the Carpathians. Geomorphology, n. 50. Elsevier. 2002. Pg. 293-306.
```
The post Como criar perfis longitudinais de rios usando QGIS e Python? first appeared on Blog 2 Engenheiros.
22 February 2021
17:41
GeoTools Team: GeoTools 23.5 released
sur Planet OSGeo

The GeoTools team is pleased to share the availability of GeoTools 23.4 :geotools-23.4-bin.zipgeotools-23.4-doc.zipgeotools-23.4-userguide.zipgeotools-23.4-project.zipThis release is published to the osgeo maven repository, and is made in conjunction with GeoServer 2.17.5. This is a maintenance release and is a recommended upgrade for all users of the GeoTools library. This is the final
1:00
GeoServer Team: GeoServer 2.17.5 Released
sur Planet OSGeo
We are pleased to announce the release of GeoServer 2.17.5 with downloads ( war | bin ), documentation and extensions .

This release is made in conjunction with GeoTools 23.5. This is a maintenance release recommended for production systems.

The GeoServer 2.17.x has reached end-of-life and this is the last scheduled release fo the 2.17.x branch. Production systems are advised to use 2.17.5 release as a temporary measure, and schedule your upgrade to 2.18.

Thanks to everyone who contributed, and Gabriel Roldan & Jody Garnett (GeoCat) for making this release.
Improvements and Fixes
Fixes included in this release:
- GEOS-9879 - app-schema extension fix for feature collection count
- GEOS-9897 - JTS upgrade breaks geofence integration
- GEOS-9880 - Monitor failure when maxSize is set to unbound
- GEOS-9881 - SldService failure when percentages and continuous parameters both set to true
- GEOS-9895 - Override transformation operations ignored for bounding box computation
- GEOS-9911 - Params-extractor plugin, wrong url in getCapabilities when having context with addition “/”
For more information check the 2.17.5 release notes.
About GeoServer 2.17
Features, presentations and reference material on the 2.17 series:
- New security tab on each layer, layer group and workspace page
- Option to use date created and date modified to sort UI lists
- New resource browser extension
- New Mapbox style extension
- FOSDEM GeoServer Orientation presentation (slides video)
- Full OSM data directory for GeoServer available on GitHub
- Code of Conduct
- Release Notes (2.17.5 2.17.4| 2.17.3| 2.17.1| 2.17.0| 2.17-RC)
21 February 2021
0:24
From GIS to Remote Sensing: Major Update: Semi-Automatic Classification Plugin v. 7.7.0
sur Planet OSGeo

This post is about a new update of the Semi-Automatic Classification Plugin v. 7.7.0.
Following the changelog:-added undo and redo buttons in SCP Dock to undo the last 10 changes in the ROIlist-added alternative search for Sentinel-2 image-the backup file of training input is created when the QGIS is closed-fixed row height in tables-fixed Sentinel-1 preprocessing issue
This version adds two buttons in the SCP dock for the undo and redo of ROI creation in the ROI & Signature list. It is possible to undo a maximum of 10 actions.This can be useful for restoring a previous version of ROIs.

In the tab Download products a new alternative search service for Sentinel-2 has been added. This service doesn't require any authentication, and uses the CREODIAS Finder API (from [https:]] the database is accessible free and anonymously, and open for anonymous access to everyone, no authorization is used). You can find information about Copernicus DIAS platforms at [https:]] .
If the option is checked, it is possible to search Sentinel-2 images without authentication.

For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin.
19 February 2021
1:00
Lutra consulting: Contributors to the point cloud campaign
sur Planet OSGeo
QGIS 3.18 is finally here. This will be the first release of QGIS with native support for point cloud data. This work was made possible by generous contributions from the community.

(Data from UGKK SR, made by Tibor Lieskovsky)
Contributors
When we announced the campaign in August 2020, the response was overwhelming and within weeks, we managed to reach and then exceed the amount required.

Below is the list of contributors in no particular order:

Mapfly, Ujaval Gandhi from Spatial Thoughts, BNHR, Imapct GIS, Andreas Neumann, Kanton Solothurn, Switzerland, City of Vevey, Mapping Automation, Service de la géomatique du canton de Neuchâtel, Hans van der Kwast, 3DGeoCloud, Rudaz+Partner AG, Leonard Gouzin, TileDB, Inc.

We wish to also thank the many anonymous contributors who do not appear in this list.

Note: if you have contributed to the campaign but your name does not appear here, you may not have selected the option to be listed in the campaign form. Please contact us if you’d like to be listed.

In addition to financial contributions, we’d like to extend our gratitude to all those who helped spread the word, helped with the testing and provided feedback and sample data.
New features

This is a brief summary of new features from our joint work with North Road and Hobu:
- Introduction of a new map layer type: a point cloud layer
- Load LAS or LAZ files (either by drag’n’drop or by opening files in Data Sources Manager)
- Load EPT datasets by pointing QGIS to their ept.json file (currently only supporting datasets on the local drive)
- Support for rendering point cloud data in 2D and 3D map views
- Apply various rendering styles in 2D and 3D:
  - “Attribute by Ramp” - draw data based on a single attribute and a color ramp (similar to “Graduated” styling for vector layers)
  - “RGB” - draw data using colors assigned to the points (combining red/green/blue attributes)
  - “Classification” - draw data using different colors for different classes (ground, buildings, vegetation, …), also allowing display of only desired classes
  - “Extent only” (2D only) - draw only bounding box of the point cloud
  - “Single color” (3D only) - draw all points with a single color
- Set size and shape of points
- Manually adjust scaling and offset of elevation (Z values) - if needed to match with elevation of other data
- Point cloud layer properties dialog to see metadata of the point clouds
- Identify tool supports point cloud layers and shows all attributes of picked points
- 2D and 3D views only render a subset of the point cloud for the best performance for the given view (for geeks - this is thanks to indexing to octree data structure, using EPT format written by untwine tool packaged with QGIS)
- Optimize the quality and performance of the 3D view using point budget configuration, which limits maximum amount of point rendered at any time (set to 1 million by default)
- Enable eye-dome lighting in 3D views for much better depth perception of point clouds
- New “Walk mode” camera navigation in 3D views - there is now a switch between the original “Terrain based” navigation mode and the new mode, which allow easier navigation through point cloud data
Please note that as this is the initial release (with over 10 thousand lines of new code related to point clouds), there may be still some rough edges here and there, or some data may not load or display correctly. In case you encounter any issues with the new functionality, please let us know - do not hesitate to create a new QGIS issue

(Data from Helsinki City)
Future work
This has been the start of a larger effort to bring full support for point cloud data into QGIS. We, in collaboration with North Road and Hobu are developing requirements for integrating point cloud data processing and analysis, more data formats, better visualisation, profile tools etc. in future releases of QGIS.

If you’re interested in helping shape those requirements or funding such features, please contact us at info@lutraconsulting.co.uk.
You may also like…
Input, a field data collection app based on QGIS. Input makes field work easy with its simple interface and cloud-based sync. Available on Android and iOS.
17 February 2021
18:08
geomati.co: Monitorización de obra civil mediante satélite y machine learning
sur Planet OSGeo

Hoy en día el uso de imágenes satelitales es casi obligatorio para el proceso de toma de decisiones en muchas áreas: agricultura, medio ambiente, recursos hídricos… Analizar las capturas (actuales y pasadas) de un sitio puede ser de gran importancia para mejorar la gestión y reducir los costos de inspección. Agregando inteligencia artificial (IA) y aprendizaje automático (ML) a esta ecuación mejoramos el poder de estas tecnologías hasta el punto de que prácticamente cualquier parámetro medible puede detectarse y rastrearse desde el espacio.

Siemens Gamesa Renewable Energies contrató a Taniwa y Geomatico para construir desde cero un producto innovador para monitorizar automáticamente las obras civiles de los aerogeneradores. El proyecto se desarrolló bajo el programa BIND4.0. El objetivo era rastrear el progreso de la construcción, basándonos en imágenes de satélite, detección automática de objetos e inteligencia artificial.

Geomatico estuvo a cargo del procesamiento automático de imágenes satelitales (todos los pasos, desde la adquisición hasta el análisis) y proporcionó entrenamiento (alrededor de 20.000 imágenes). Taniwa fue responsable de los algoritmos ML para identificar el estado, el desarrollo de la interfaz web y la implementación de alta escalabilidad a través de Azure Cloud.

Tecnologías: análisis raster, machine learning, GIS, React, Kubernetes
Photo by Master Wen on Unsplash
La entrada Monitorización de obra civil mediante satélite y machine learning se publicó primero en Geomatico.
16 February 2021
1:00
Lutra consulting: Test QGIS with Point Clouds on Windows
sur Planet OSGeo

In the past months, we have been busy adding support for point cloud data in QGIS (3.18). Read more on how to install and test this feature under MS Windows.
Installation
To be able to use this feature, you need extra packages and also the latest OSGeo4W installer.
Update
Since the original post, Jürgen Fischer has created a stand-alone MSI installer. You can download the new installer from the QGIS website. The installer is only for 64-bit platforms and does not support MS Windows 7.

Note that there have been several regressions and bugs with the first release of QGIS 3.18.0. The issues are being addressed and soon there will be an updated version available. The above link is only for those who are eager to test the point cloud data in QGIS.
Installation
To be able to use this feature, you need extra packages and also the latest OSGeo4W installer.

Note: This is a completely revamped and different packaging system than the current OSGeo4W installer. To avoid any clash with your current installation, it is recommended to use different paths for temporary download files and installation of the new packages. The new packaging only supports 64-bit platform.

1- Download and run the NEW OSGeo4W installer

2- Select the Advanced install and pick qgis-dev from the list of packages

Special thanks to Jürgen Fischer for his hard work on preparing the new packages for Windows.
Testing
Once installation is completed, try to run QGIS from the installation path (e.g. C:\OSGeo4W64\bin\qgis-dev.bat). You should be able to load LAS\LAZ file to your map from the Browser panel or the Data Source Manager.

The point cloud data can be visualised in 2D and 3D map canvas.

Please test and let us know if you encounter any problems when loading, viewing or styling point cloud data. The best way to do that is to create a new issue on GitHub: [https:]

QGIS 3.18 will be released later this week (February 19), so grab your copy of QGIS today and give it a try, so that we can fix any remaining issues before the release!
Troubleshooting
Problem: I am unable to add any LAS/LAZ point cloud file

Solution: Ensure you have used the correct installer linked above. Development builds of QGIS in the ordinary OSGeo4W installer DO NOT include support for point clouds.
15 February 2021
17:51
GeoSolutions: State of MapStore Release 2021.01.00 and Beyond – Free Webinar
sur Planet OSGeo

Enclosure: [download]

You must be logged into the site to view this content.
7:00
Lutra consulting: Case Study: FLO-2D QGIS Plugin
sur Planet OSGeo
The case study presents the implementation of the QGIS FLO-2D Plugin project (5 minute read)
Introduction
FLO-2D is one of the most widely used commercially available flood models. FLO-2D is capable of simulating urban flooding in high resolution including storm drain systems.

In 2016 the FLO-2D team invited us to develop a set of tools for optimising the flood model build process in QGIS. The resulting plugin allows hydraulic models to be built quickly by leveraging the wide range of tools available in the QGIS ecosystem.

The plugin can be downloaded by following the guidance in the plugin documentation

As the use of open source GIS grows within the water engineering sector, Lutra Consulting develops and maintains MDAL which makes the visualisation and post-processing of time-varying numerical model results possible in QGIS.

Contact us at info@lutraconsulting.co.uk if you’d like to discuss the benefits of integrating your flood modelling software more tightly with QGIS.

Overview of Previous GUI tools: FLO-2D GDS
FLO-2D’s focus on urban modeling requires large datasets that may include several million grid cells. Each cell has between 4 and 10 attributes so datasets can often be in the range of several gigabytes.

Previously, Grid Developer System (GDS) was used to pre-process the spatial data used by FLO-2D. Being a 32-bit application, the GDS was only able to load up to 4 gigabytes of data and its programming framework was also no longer maintained by Microsoft. Modellers could only be built on Windows PCs.

All development and maintenance of the GDS was carried out by the FLO-2D team.
Behind the Implementation Guiding principles behind the new QGIS Flo-2D plugin
We assessed the tools used at the time and in close collaboration with the FLO-2D team, optimised the workflow from the users’ point of view with emphasis on speed and simplicity.

To keep things simple for the users, we aimed to provide a level of abstraction so users would no longer need to be involved with the internal structure of FLO-2D solver input files. They could instead focus on real-world aspects affecting their models.

Together with the FLO-2D team we designed a solution that was based on 3 core ideas:
- Modellers will use native QGIS tools and point/line/polygon layers to define real-world objects (e.g. domain, boundary conditions, levees, …)
- These real-world objects will be converted automatically into the data structures required by the FLO-2D solver (although expert users can still modify these data structures if they so wish).
- Additional productivity tools will be provided to allow users to speed-up time consuming tasks
Features of the plugin
Some of the key features of the solution are listed here:
- Easy creation and handling of new models (all model data sits in a single GeoPackage file)
- User friendly and intuitive digitizing and manipulation of model components (through simple GIS layers and dedicated tools) including:
  - 1D domain
  - Boundary and initial conditions
  - 1D channels and cross-sections
  - Levees
  - Rainfall
  - Infiltration areas
  - Storm drains
- Various options for obtaining grid data such as elevation, roughness, reduction factors etc. from different sources (raster layers, external vector layers etc.)
- Plotting profiles and editing time series data
- Automated tools for schematizing input layers into the GDS format
- Importing basic data from HEC-RAS models
- Import / export functionality between GeoPackage and GDS format (*.DAT files)
- Running external FLO-2D tools (FLO-2D Pro engine etc.) directly from QGIS
Benefits for the FLO-2D developers:
Benefits for the developers of FLO-2D include:
- Reduced development and maintenance costs, since much of the heavy lifting of the FLO-2D plugin is done by QGIS itself
- By being part of the QGIS ecosystem, gaining opportunities to approach QGIS users in the flood risk industry to use FLO-2D software
- The FLO-2D plugin is developed on GitHub, allowing the latest development technologies such as continuous integration, automatic testing and issue tracking to be used
- Ability to solve upstream bugs in QGIS or MDAL due to the open-source nature of the projects
Benefits for FLO-2D users:
Some of the benefits realised by FLO-2D customers include:
- Being able to work with their FLO-2D models using open source GIS on all major operating systems
- A full GIS application to support their data pre-processing
- Logical and intuitive workflows
- FLO-2D results can now be visualised and post-processed natively in QGIS via mesh layer
- Ability to use all native QGIS support and development channels in addition to FLO-2D support
- Integration of internal workflows with powerful native QGIS features including projection support, GDAL/OGR integrations, background maps support (e.g. vector tiles), printed flood maps, etc.
- Ability analyze results via QGIS’ Crayfish plugin and produce graphs and outputs
The plugin in action! Further Reading
- FLO-2D webpage
- MDAL and Mesh Layer
Do you have any questions or would like to see demo of QGIS Mesh Layer? Contact us at info@lutraconsulting.co.uk or schedule a demo call calendly.com/saber-razmjooei/15min
Key words
QGIS, plugin, python, migration, optimised, speed up, fast, hydraulic modelling, water, 2D, open-source, cost reduction, software development
12 February 2021
18:53
Free and Open Source GIS Ramblings: Movement data in GIS #32: “Exploring movement data” webinar
sur Planet OSGeo

Last October, I had the pleasure to speak at the Uni Liverpool’s Geographic Data Science Lab Brown Bag Seminar. The talk starts with examples from different movement datasets that illustrate why we need data exploration to better understand our datasets. Then we dive into different options for exploring movement data before ending on ongoing challenges for future development of the field.

Here’s the full recording of my talk and follow-up discussion:

This post is part of a series. Read more about movement data in GIS.
13:44
From GIS to Remote Sensing: Major Update: Semi-Automatic Classification Plugin v. 7.6.0
sur Planet OSGeo

This post is about a new update of the Semi-Automatic Classification Plugin v. 7.6.0.
Following the changelog:-training input vector is saved as gpkg-export signatures as gpkg-better compatibility with custom projections-general use of gpkg-fixed issue in DOS1 correction-attempt to fix subprocess issue in Windows

This version changes the structure of training input .scp files replacing the shapefile with GeoPackage.Previously created .scp files can be opened and the structure will be automatically changed during ROI saving or project saving
!Attention! backup old training input .scp files before opening it in this new version to prevent data loss.
Also, training input can be exported as GeoPackage.
Another improvement is compatibility with custom projections (not only EPSG standards), which can be used in input band sets.
Finally, an issue related to DOS1 algorithm has been fixed. If you previously used DOS1 for image correction I recommend to perform the preprocessing again. I apologize for the inconvenience.
For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin.
11 February 2021
16:49
Free and Open Source GIS Ramblings: Introducing the open data analysis OGD.AT Lab
sur Planet OSGeo
Data sourcing and preparation is one of the most time consuming tasks in many spatial analyses. Even though the Austrian data.gv.at platform already provides a central catalog, the individual datasets still vary considerably in their accessibility or readiness for use.

OGD.AT Lab is a new repository collecting Jupyter notebooks for working with Austrian Open Government Data and other auxiliary open data sources. The notebooks illustrate different use cases, including so far:
1. Accessing geodata from the city of Vienna WFS
2. Downloading environmental data (heat vulnerability and air quality)
3. Geocoding addresses and getting elevation information
4. Exploring urban movement data
Data processing and visualization are performed using Pandas, GeoPandas, and Holoviews. GeoPandas makes it straighforward to use data from WFS. Therefore, OGD.AT Lab can provide one universal gdf_from_wfs() function which takes the desired WFS layer as an argument and returns a GeoPandas.GeoDataFrame that is ready for analysis:

Launch this notebook in: [https:]]

Many other datasets are provided as CSV files which need to be joined with spatial datasets to use them in spatial analysis. For example, the “Urban heat vulnerability index” dataset which needs to be joined to statistical areas.

Launch this notebook in Mybinder: [https:]]

Another issue with many CSV files is that they use German number formatting, where commas are used as a decimal separater instead of dots:

Besides file access, there are also open services provided by other developers, for example, Manfred Egger developed an elevation service that provides elevation information for any point in Austria. In combination with geocoding services, such as Nominatim, this makes is possible to, for example, find the elevation for any address in Austria:

Launch this notebook in MyBinder: [https:]]

Last but not least, the first version of the mobility notebook showcases open travel time data provided by Uber Movement:

Launch this notebook in Mybinder: [https:]]

The utility functions for data access included in this repository will continue to grow as new data sources are included. Eventually, it may make sense to extract the data access function into a dedicated library, similar to geofi (Finland) or geobr (Brazil).

If you’re aware of any interesting open datasets or services that should be included in OGD.AT, feel free to reach out here or on Github through the issue tracker or by providing a pull request.
10:00
CARTO Blog: Announcing #SDSC21: A Series of Spatial Data Science Events
sur Planet OSGeo

Having brought together more than 5,000 experts online in 2020, we’re excited to announce 2021’s online edition of the Spatial Data Science Conference, gathering Data Scien...
10:00
CARTO Blog: Announcing CARTO BigQuery Tiler General Availability
sur Planet OSGeo

Introducing BigQuery Tiler
9:00
CARTO Blog: Agritech & Spatial Data: Predicting Crop Yield in Rural Australia
sur Planet OSGeo

This post was written by NGIS, our master reseller in Australia and New Zealand and was originally published on their website.
10 February 2021
9:30
Andrea Antonello: HydroloGIS turns 16 + joining Aries
sur Planet OSGeo

Today it’s HydroloGIS’ birthday. We count 16 years of passionate open source engagement in the fields of GIS and environmental engineering, against the odds of those that told us in the face that you can’t live on open source and share your work and your knowledge for free.
Well, in our 16th birthday we have one big news to share. It is with great pleasure that we announce the start of our collaboration with the team of Artificial Intelligence for Environment and Sustainability (in short ARIES ) of the Basque Center for Climate Change. With this collaboration we finally close a circle going back to our roots: integrated modelling for the environment. This is how we started over 15 years ago, developing and maintaining the open source project of the Hortonmachine. Having the possibility to join what we think is the most important open source project addressing integrated modelling, is huge for us. Silvia will be working on water related modelling, mostly hydrology and hydraulics. Andrea will be joining the klab core engine team as a GIS expert.
So don’t be confused if you find us on the Aries page. We are still HydroloGIS, but with a beautiful breeze of research that flows steadily through our souls. :-) And so that it is clear, the Hortonmachine project will be strengthened by this collaboration (klab already uses the Hortonmachine modules) and the SMASH and Geopaparazzi projects will move on as they were doing before, in case you were worried.
Ahhh, life's goood!
9:00
Lutra consulting: Quickly Get Site Photos Into Word
sur Planet OSGeo
Preparing reports with lots of survey photos takes time - this plugin automates the process.

Nowadays, it’s really easy to take georeferenced photos on site visits. Tools like Input can capture photos, descriptions and location information and bringing it all into GIS is straight-forward. However.. I recently discovered that people still spend significant amounts of time organising their photos into reports outside of GIS. This small plugin automates the process of getting the photos and their descriptions into Microsoft Word.

For this blog post I knocked-up a simple survey project based on the Field notes Mergin template. Points can have a photo, title and description amongst other fields. No comments on my gardening skills please!

When I sync the data back to QGIS, the attribute table looks something like this:

We’ll now use the HTML Table Exporter plugin to export the layer as an HTML table (this will let us get it into Word).

First install the plugin.

It’s an experimental plugin so you’ll first need to check the Show also experimental plugins option under Plugins > Manage and Install Plugins… > Settings.

When you have installed the plugin, open it using this button or via Plugins > HTML Table Exporter > Export table as HTML.

You should now see this:

Set Table to the layer you want to export. The image scaling option is described later, leave it at a small setting for the time being. Click OK to export and tell the plugin where to save the HTML file.

Now the data should be out of QGIS.. the next steps are in.. MS Word :o

Right click on the exported html file and open it with Microsoft Word:

Let’s see what it looks like:

OK.. we’re getting somewhere! At this point you’ll want to:
- Enable Print Layout mode in Word so you can see what the printed page will look like
- Set the page orientation as desired (I chose landscape in the image above)
- Experiment with image rotation if required (Right click photo > Size and position > Rotation)
If playing with rotation, just focus on rotating a single image, we’ll batch rotate later as required. For now you want to get a feel of whether you want to adjust the scale factor in QGIS (to make the images smaller or larger) to save you having to resize them individually in Word.

I’ve decided to increase the scale factor from 10% to 15% so will now re-export.

Beware that Word has an exclusive lock on the html file when it’s open so you need to close it in Word before you can export it again from QGIS.

I settled for 10% in the end so I could get multiple images on each page in portrait mode. After removing the columns I didn’t want, the table in Word now looks like this:

To rapidly rotate images by 90 degrees, rotate the first one using Right click on photo > Size and position > Rotation then select subsequent photos and press the F4 key. This method is described in more detail here as well as other Word batch image rotation methods.

My document is almost finished. There are just a few small issues to iron out. Currently, the images are referenced by the Word document, not embedded. This means if I email the document to someone, the images will be missing. Let’s fix that by embedding the images in the word document.

First save the document as a Word document in Word’s native format (e.g. *.docx).

Next, locate the Edit Links to Files option:

Select all the linked images (the shift and arrow keys help here) and check the Save picture in document option and click OK:

Save the document, your photos should now be embedded within the document.

If you find your word document gets huge, you can use the method here to quickly batch compress all images in the document.

Input is a free and open source field data collection and mobile GIS app based on QGIS.

If this guide saved you some time and you feel like doing something awesome for us, a review of Input on the Apple App Store or Google Play Store would be really well appreciated.
09 February 2021
21:21
OTB Team: Release of OTB 8.0 Alpha 1
sur Planet OSGeo

Dear OTB community, We are happy to announce that OTB version 8.0.0 Alpha 1 is available. The main goal of release 8.0 is to remove OSSIM from OTB dependencies. This implies the refactoring of a lot of classes and functions, many of which are in the core modules of the library. The plan of this […]
05 February 2021
22:12
GRASS GIS: Elections results and new GRASS GIS PSC
sur Planet OSGeo

New GRASS GIS Project Steering Committee By the end of last year, the GRASS GIS project called for PSC members election. A total of 13 GRASS GIS contributors were nominated by the community to cover the nine PSC positions. After the election itself, the new GRASS GIS PSC is composed of the following nine members ranked by number of votes: Markus Neteler (95) Anna Petrášová (88) Helena Mitášová (86) Martin Landa (83) Verónica Andreo (76) Moritz Lennert (74) Václav Petráš (68) Michael Barton (58) Huidae Cho (56) For completeness, all relevant candidacy communications, as well as details about the voting process, have been published at: [https:]
04 February 2021
14:35
GeoSolutions: Upcoming INSPIRE Free Workshop: Smart Data Loader and Templating for GeoServer
sur Planet OSGeo

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1:24
From GIS to Remote Sensing: Random Forest Classification using the Semi-Automatic Classification Plugin
sur Planet OSGeo
This tutorial is about the Random Forest classification using the Semi-Automatic Classification Plugin (SCP) for QGIS. It is assumed that one has the basic knowledge of SCP and Basic Tutorials.
Random Forest is a particular machine learning technique, based on the iterative and random creation of decision trees (i.e. a set of rules and conditions that define a class).
WARNING: ESA SNAP is required. The ESA SNAP GPT executable must be defined in External programs settings.
The purpose of the classification is to identify the following land cover classes:
1. Water;
2. Built-up;
3. Vegetation;
4. Soil.
The following are the steps of the tutorial:
1. Input Data
2. Create the ROIs
3. Random Forest Classification
Following the video of this tutorial.

Read more »
03 February 2021
18:22
GeoSolutions: MapStore Release 2021.01.00
sur Planet OSGeo

Enclosure: [download]

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9:10
gvSIG Team: 30?????gvSig?????
sur Planet OSGeo

?????????30???????gvSIG??????

?????????????????????????????????????????????? ?????????shapefile???????? ???????????????????shapefile??????????????????????????????????????????…

????????????????????“????”???????
02 February 2021
19:42
Martin Davis: OverlayNG and Invalid Geometry
sur Planet OSGeo

A recent blog post by Elephant Tamer gives a critical appraisal of the improvements to overlay processing shipped in PostGIS 3.1 with GEOS 3.9. The author is disappointed that PostGIS still reports errors when overlay is used on invalid geometry. However, this is based on a misunderstanding of the technology.
GEOS 3.9 includes OverlayNG, ported from the JTS Topology Suite). It brings a major advance in overlay robustness, along with other improvements (described here and here). Previously, robustness limitations in the overlay algorithm could sometimes cause errors even for inputs which were topologically valid. This was doubly problematic because there was no fully effective way to process the input geometries to avoid the errors. Now, OverlayNG solves this problem completely. Valid inputs will always produce a valid and essentially correct(*) output.
(*) "Essentially" correct, because in order to achieve full robustness a snapping heuristic may be applied to the input geometry. However, this is done with a very fine tolerance, so should not appreciably alter the output from the theoretically correct value.
But for invalid inputs, OverlayNG will still report errors. The reason is that there is a wide variety of gruesome ways in which geometry can be invalid. Automated handling of invalidity would involve expensive extra processing, and also require making assumptions about what area a corrupt geometry is intended to represent. Rather than silently repairing invalid geometry and returning potentially incorrect results, the design decision is to report this situation as an error.
In fact, OverlayNG is able to handle "mildly" invalid polygons, as described in this post. This covers situations which are technically invalid according to the OGC SFS specification, but which still have well-defined topology. This includes self-touching rings (sometimes called "inverted polygons" or "exverted holes"), and zero-width gores and spikes.
Taking a detailed look at the data used in the blog post, we can see these improvements at work. The dataset is the ecology polygons obtained from the GDOS WFS server. This contains 7662 geometries, of which 10 are invalid. Using the old overlay algorithm, 9 of these invalid polygons cause TopologyException errors. Using OverlayNG, only 4 of them cause errors.
The polygons that can now be processed successfully are typical "OGC-invalid" situations, which do not materially affect the polygonal topology. These include self-touching rings with pinch points:
and zero-width gores:

Of the cases that still cause errors, two are classic small bow-tie errors:

And two are wildly invalid self-crossing rings:

The last two are good examples of geometry which is so invalid that it is impossible to unambiguously decide what area is represented (although ST_MakeValid will happily grind them into something that is technically valid).
Ultimately it is the user's responsibility to ensure that geometries to be processed by overlay (and many other PostGIS functions) have valid topology (as reported by ST_IsValid). Ideally this is done by correcting the data at source. But it can also be done a posteriori in the database itself, by either the ST_MakeValid function, or the well-known buffer(0) trick. (Which to use is a topic for another blog post...)
One improvement that could be made is to check for input validity when OverlayNG throws an error. Then PostGIS can report definitively that an overlay error is caused by invalid input. If there is an overlay error that is not caused by invalidity, the PostGIS team wants to hear about it!
And perhaps there is a case to be made for repairing invalid geometry automatically, even if the repair is suspect. Possibly this could be invoked via a flag parameter on the overlay functions. More research is required - feedback is welcome!
01 February 2021
9:00
Narcélio de Sá: TecnoloGEO 27 – GeoMarketing
sur Planet OSGeo
Neste podcast: O TecnoloGEO 27 traz até você um bate-papo com Narcélio de Sá, Murilo Cardoso, Luís Sadeck e Tiago Sousa sobre GeoMarketing. Nesse episódio iremos fazer um apanhado geral do tema falando sobre: conceitos, aplicações, limitações e dicas para conhecer um pouco mais e quem sabe seguir carreira na área.

O Geomarketing pode ser definido como o meio onde o conhecimento geográfico e as técnicas de análise espacial se encontram com estratégias de marketing, buscando diversos objetivos a nível comercial: expansão de rede, estratégias assertivas, aumento das vendas… entre outros.
Aperte o ? para começar o episódio
O TecnoloGEO é um podcast encabeçado por Murilo Cardoso e Narcélio de Sá. O Podcast é mais uma iniciativa que visa informar, instruir e, porque não, divertir pessoas que se interessam pela área de Geotecnologias.
Referências e Indicações do TecnoloGEO 27:
Sugestões de literatura:
- Livro: Minimalismo Digital: Para uma Vida Profunda em um Mundo Superficial – Cal Newport
- Livro: Geomarketing: memórias de viagem – Francisco José Espósito Aranha Filho
- Livro: Geomarketing: Methods and Strategies in Spatial Marketing (Biblia do Geomarketing) – Gérard Cliquet
- Livro: Coleção Minha Capital – Minha Belém n. 1 – IBGE
- Livro: Sociedade do cansaço – Byung-Chul Han
- Livro: A loucura da razão econômica: Marx e o capital no século XXI – David Harvey
- Livro: O Direito à Preguiça – Paul Lafargue
- Dissertação: Construção de um Modelo de Análise Espacial em SIG, que determine a Localização Ótima de Equipamentos Sociais para idosos, no Concelho de Lisboa – Rita Canha Martins
- Dissertação: Modelo de Geomarketing e Estatística Espacial para Gestão das Recolhas do Instituto Português do Sangue e da Transplantação – Daniela Alexandra de Oliveira Figueiredo
Sugestões de séries e Filmes:
- Série: Lupin (2021) – Netflix
- Série: Mad Men: Inventando Verdades (2007) – AMC
- Série: Rotten (2018) – Netflix
- Filme: A Vastidão da Noite (2020) – Prime Vídeo
Nota de transparência: os links dos livros acima têm código de afiliado. Clicando neles, os preços não mudam, mas eu posso ganhar uma comissão da Amazon.

Fale conosco! E não esqueça de deixar o seu comentário na postagem desse episódio!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:
Narcélio de SáLeonardo
João AtaídeMichelangelo
Murilo CardosoRaphael
Tiago SouzaDonatello
Luis SadeckMestre Splinter
The post TecnoloGEO 27 – GeoMarketing appeared first on Narcélio de Sá.
28 January 2021
11:00
MapTiler: OpenMapTiles 3.12: improved city centers
sur Planet OSGeo

The most visible change in our open-source map publishing project OpenMapTiles 3.12 is in rendering buildings on zoom level 13, adding new features to the water layer and a few South- and East-Asian languages.

Also, a lot of effort was invested into improving the quality of tools which made the workflow of the project much more simple, straightforward, and user-friendly.
Generalized urban settlement
The most visible change in OpenMapTiles 3.12 is the reworked building layer on the zoom 13. The brand-new algorithm creates blocks of buildings before it generalizes them. This leads to more accurate visualization over cities and villages.

Generalized building on zoom level 13 shows much accurate data in cities
More detailed airport maps
To render airfields even more precisely, airport gates have been added to the schema. Moreover, there were additional bugfixes and cleanups related to airports.

Gates at Charles-de-Gaulle airport
Enhanced water layer with swimming pools
New types of water bodies have been added to the water layer, namely swimming pools, salt ponds, and basins (leisure=swimming_pool, landuse=basin, and landuse=salt_pond as they are represented in the OpenStreetMap).

Salt ponds in the SF Bay Area are now visible in OpenMapTiles 3.12

Drinking water (OSM tag amenity=drinking_water) has been added into the POI layer.
Better rendering of protected areas
Some time ago, the OpenStreetMap community decided to change tagging of protected areas to boundary=protected_area. To keep up with them, it is now rendered in the park layer.

National parks in the Alps
Languages of India, Romanized Japanese and Korean
There are new supported languages including Hindi, Tamil, Telugu, Hiragana Japanese + Romanized Japanese and Korean. In total, the OpenMapTiles project currently supports 70 languages.

English, Hindi, and Tamil labels on the map of India
Simple highlighting of national borders
New preprocessing of national borders now joins borders into one piece and marks the state on the left and on the right. This change allows easy manipulation with border data in the style editor?—?e.g. for highlighting borders of a selected country.

Technical improvements under the hood
The main “invisible” technical enhancement of OpenMapTiles 3.12 is the lower number of required Docker images needed for run. Instead of importing each layer separately, they are now unified into OpenMapTiles-Tools with just four main docker containers (postgis, openmaptiles-tools, import-data and generate-tiles).

All workflows are now under GitHub Action testing process. These tests provide integrity tests and performance tests of all changes.

The Makefile has been extensively upgraded and cleaned up. This improvement makes easier to run checks on more areas and definition files.

There is also remarkably improved processing of Wikidata, which now uses a SPARQL endpoint for retrieving Wikidata instead of downloading and parsing the whole database dump.

To rise performance and create more precision results, a number of SQL-related changes were done.

See all improvements in the official changelog.
Thanks to the community members involved
We would like to send a big thanks to our community members who are involved over a long period, namely Yuri, Frederic, Zeev, Jorge, Taro, Sergii, Jan-Phillipp, and others for contributing into 3.12.
Get new release via Maps API or download Data
Data with above described changes are available on MapTiler Cloud via Maps API or as a downloadable data package in MapTiler Data.
8:00
From GIS to Remote Sensing: Unsupervised Classification using the Semi-Automatic Classification Plugin version 7
sur Planet OSGeo
This is a basic tutorial about the use of Semi-Automatic Classification Plugin (SCP) for the unsupervised classification of a multispectral image. It is recommended to read the Brief Introduction to Remote Sensing before this tutorial, and in particular the part Clustering.
Clustering can be used for unsupervised classification, which means that no training input is required, producing classes (i.e. clusters) that have no definition and consequently the user must assign a land cover label to each class.
The purpose of the classification is to identify the land cover classes with the corresponding ID codes defined in the following table.
Classes
Class name Class ID
Water 1
Built-up 2
Vegetation 3
Soil 4

The following are the main steps of this tutorial:
1. Input Data
2. Clustering
3. Reclassification of the output
4. Refinement of the output
Following the video of this tutorial.

Read more »
1:00
PostGIS Development: PostGIS 3.1.1
sur Planet OSGeo
The PostGIS Team is pleased to release the release of PostGIS 3.1.1!

This release is a bug fix release, addressing issues found in the previous 3.1 release.
- #4814, Crash passing collection with only empty components to ST_MakeValid
- #4818, Make the VSICURL synthetic driver work as documented
- #4825, Unstable results from ST_MakeValid
- #4823, Avoid listing the same geometry in different collections
Continue Reading by clicking title hyperlink ..
1:00
SourcePole: FOSSGIS-UPDATE 2021
sur Planet OSGeo

An der Erstausgabe des Online-Events FOSSGIS-UPDATE war Sourcepole mit dem Vortrag “Game Engines - die Zukunft von 3D GIS?” vertreten.

Die Folien zum Vortrag sind verfügbar und die live gezeigte Godot-Engine Demo kann unten in der Web-Version angschaut werden.
27 January 2021
20:05
GeoSolutions: GeoSolutions Open to Partnership Worldwide
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12:00
Fernando Quadro: PostGIS 3.1 – Melhorias no Vector Tiles
sur Planet OSGeo

Cada dias mais várias empresas e pessoas estão usando as funções Vector Tile do PostGIS como backend para mapas vetoriais dinâmicos, dessa forma qualquer melhoria terá um grande impacto. É por isso que, desde o aparecimento das funções MVT no PostGIS 2.4, elas foram aprimoradas em cada versão principal, e 3.1 não seria diferente.

Como o ST_AsMVT torna realmente fácil extrair informações do banco de dados para o navegador, uma armadilha comum é usar SELECT * para extrair todas as colunas disponíveis que podem mover muitos dados desnecessariamente e gerar blocos extremamente grandes. A solução fácil para esse problema é selecionar apenas as propriedades necessárias para a visualização, mas pode ser difícil aplicá-la retroativamente uma vez que o aplicativo/aplicação já esteja em ambiente de produção e já dependa do design ineficiente.

Ao investigarem o por que o OOM estava interrompendo o bancos de dados, descobriram consultas que estavam usando uma quantidade enorme de recursos para gerar blocos de 50 a 100 vezes maiores do que deveriam (a recomendação é menor que 500 KB).

Neste caso, o mau design de extrair todas as colunas do conjunto de dados foi agravado pelo fato de estar sendo aplicado a um grande conjunto de dados; esse paralelismo do PostgreSQL disparou, exigindo recursos extras para gerar blocos em paralelo e posteriormente mesclá-los.

No PostGIS 3.1 foi então introduzidas várias mudanças para melhorar o desempenho dessas 2 etapas: o processamento paralelo e a fusão de resultados intermediários.

1. As mudanças

Sem entrar em muitos detalhes, o principal benefício veio de alterar o bloco de vetor da forma .proto, para que um recurso possa conter apenas um valor de cada vez. Isso é o que a especificação diz, mas não o que .proto obriga, portanto, a biblioteca interna estava alocando memória que nunca usou.

Existem outras mudanças adicionais, como melhorar a forma como os valores são mesclados entre os workers paralelos, portanto, fique à vontade para dar uma olhada no próprio commit final se quiser mais detalhes.

2. Comparação de desempenho

A melhor maneira de ver o impacto dessas mudanças é por meio de alguns exemplos. Em ambos os casos foi gerado o mesmo bloco, no mesmo servidor e com as mesmas dependências; a única mudança foi substituir a biblioteca PostGIS, que de 3.0 para 3.1.

No primeiro exemplo, o bloco contém todas as colunas dos 287 mil pontos nele. Como mencionado antes, não é recomendável fazer isso, mas é a consulta mais simples de gerar.

E para o segundo exemplo foi gerado o mesmo bloco, mas agora incluindo apenas as colunas mínimas para a visualização:

Podemos ver, tanto no PostGIS 3.0 quanto no 3.1, que adicionar apenas as propriedades necessárias torna as coisas 10 vezes mais rápidas do que com os dados completos, e também que o Postgis 3.1 é 30-40% mais rápido em ambas as situações.

3. Uso de memória

Além da velocidade, essa mudança também reduz muito a quantidade de memória usada para gerar um bloco.

Para vê-lo em ação, foi monitorado o processo PostgreSQL enquanto ele está gerando o bloco com todas as propriedades. No 3.0, observamos na linha azul que o uso de memória aumenta com o tempo até atingir cerca de 2,7 GB no final da transação.

Agora foi monitorada a mesma solicitação em um servidor usando Postgis 3.1. Neste caso, o servidor usa cerca de um terço da memória como no 3.0 (1GB vs 2.7GB) e, em vez de ter um aumento linear, a memória é devolvida ao sistema o mais rápido possível.

Para resumir tudo: PostGIS 3.1 é mais rápido e usa menos memória ao gerar grandes blocos vetoriais.

Este post foi escrito originalmente escrito por Raúl Marín, e traduzido e adaptado livremente por este blog.

Fonte: Clever Elephant Blog
26 January 2021
10:00
CARTO Blog: How to Improve Retail Trade Area Accuracy with Mobility Data
sur Planet OSGeo

In the retail industry a trade area, also known as a catchment area, is the geographic area from where you draw your customers. There are a number of methods for calculatin...
25 January 2021
12:00
Fernando Quadro: PostGIS 3.1 – Performance
sur Planet OSGeo
Os desenvolvedores de código aberto às vezes têm dificuldade em descobrir em qual recurso se concentrar, a fim de gerar o valor máximo para os usuários finais. Como resultado, muitas vezes eles terão desempenho padrão .

Desempenho é o único recurso que todo usuário aprova. O software continuará fazendo as mesmas coisas legais, só que mais rápido.

Para o PostGIS 3.1, houve uma série de melhorias de desempenho que, juntas, podem resultar em um ganho de desempenho substancial para suas cargas de trabalho.

1. Cache de grandes geometrias

As junções espaciais foram eram lentas pela sobrecarga do acesso a grandes geometrias por um longo tempo.
```
SELECT A.*, B.*
FROM A
JOIN B
ON ST_Intersects(A.geom, B.geom)
```
Para o SQL acima, o PostgreSQL planejará e executará junções espaciais como essa usando uma “junção de loop aninhada”, o que significa iterar por um lado da junção e testar a condição de junção. Isso resulta em execuções que se parecem com:
- ST_Intersects (A.geom (1), B.geom (1))
- ST_Intersects (A.geom (1), B.geom (2))
- ST_Intersects (A.geom (1), B.geom (3))
Portanto, um lado do teste se repete indefinidamente.

Armazenar esse lado em cache e evitar reler o objeto grande a cada iteração do loop faz uma grande diferença no desempenho. Vimos acelerações 20 vezes maiores em cargas de trabalho de junção espacial comum .

As correções são bastante técnicas, mas se você estiver interessado, tem um artigo do Paul Ramsey detalhado disponível em inglês.

2. Leituras apenas do cabeçalho da geometria

O formato em disco para geometria inclui um cabeçalho curto que possui informações sobre os limites da geometria, o sistema de referência espacial e dimensionalidade. Isso significa que é possível para algumas funções retornar uma resposta depois de ler apenas alguns bytes do cabeçalho, em vez de todo o objeto.

No entanto, nem todas as funções que poderiam fazer uma leitura rápida, era possível fazer uma leitura rápida. Isso agora está resolvido.

3. Geração mais rápida de texto

É muito comum que aplicativos da web e outros gerem formatos de texto dentro do banco de dados, e o código para fazer isso não foi otimizado. A geração de “texto conhecido” (WKT), GeoJSON e saída KML agora usa um caminho mais rápido e evita cópias desnecessárias.

PostGIS agora também usa o mesmo código de número para texto que o PostgreSQL, que se mostrou mais rápido, e também nos permite expor um pouco mais de controle sobre a precisão aos usuários finais.

4. Quão mais rápido?

Para o caso de uso específico de união espacial, aqui está um caso de teste:

1:10 milhões – Limites de país
1:10 milhões – Lugares povoados

Carregue os dados em ambas as versões:
```
shp2pgsql -D -s 4326 -I ne_10m_admin_0_countries admin | psql postgis30
shp2pgsql -D -s 4326 -I ne_10m_populated_places places | psql postgis30
```
Execute uma junção espacial que encontre a soma dos locais povoados em cada país.
```
EXPLAIN ANALYZE
SELECT Sum(p.pop_max) as pop_max, a.name
FROM admin a
JOIN places p
ON ST_Intersects(a.geom, p.geom)
GROUP BY a.name
```
Tempo médio em 5 execuções:
- PostGIS 3.0 = 23.4s
- PostGIS 3.1 = 0.9s
Este teste é uma espécie de “pior caso”, em que existem muitos países muito grandes, mas dá uma ideia dos tipos de acelerações que estão disponíveis para junções espaciais contra coleções que incluem maiores (+250 pares de coordenadas) geometrias.

Este post foi escrito originalmente por Paul Ramsey e traduzido e adaptado livremente por este blog.

Fonte: Clever Elephant Blog
23 January 2021
20:46
From GIS to Remote Sensing: Major Update: Semi-Automatic Classification Plugin v. 7.5.0
sur Planet OSGeo

This post is about a new update of the Semi-Automatic Classification Plugin v. 7.5.0. Following the changelog:
-added context menu in SCP dock-fixed issue with SCP dock editing of macroclasses-fixed issue with classification band set-fixed issue with autosave ROI
The main new feature is the context menu in the SCP dock that can be accessed with a right click in the ROI & Signature list.

The context menu allows for several functions that can be performed for highlighted items, such as merging or calculating the spectral signatures, or managing the tree.

Also, other issues were fixed.In the next few days I'm going to publish a new tutorial about clustering.
For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin.
22 January 2021
10:00
CARTO Blog: 3 Spatial Data Science Trends to Watch in 2021
sur Planet OSGeo

Geography is changing faster than ever before. Global warming and COVID-19 are pushing change at an unprecedented speed, modifying our environments, markets, and society. T...
21 January 2021
10:00
CARTO Blog: Identifying Areas Most Likely to Suffer Recession Post Pandemic
sur Planet OSGeo

Due to the current level of uncertainty surrounding the Covid-19 pandemic, the effects of the upcoming economic recession are nearly impossible to predict; however, history...
2:00
Lutra consulting: Overview of QGIS 3.16 LTR and QGIS 3.18 Mesh Features
sur Planet OSGeo
QGIS Mesh Layer now support DHI dfsu and HECRAS 6.0 format.

The releases of QGIS 3.16 LTR/QGIS 3.18, MDAL 0.8.0 and Crayfish 3.5.0 are planned for 19 February 2021. We are delighted to present the following improvements for the upcoming releases:
- In-memory mesh datasets with persistence
- Multi identify tool for mesh
- Virtual dataset groups for mesh layer
- 3D Mesh Layer Bug Fixes and speed optimizations
- DHI’s dfsu format support (QGIS 3.18 only)
- HECRAS 6.0 format support
If you’d like try the latest features, you can always install QGIS nightlies/master, which comes with all the latest features described in this blog post.

If you want to learn more about Mesh Layer in QGIS, read more here…
Mesh Calculator and Layer Improvements
The Virtual dataset groups for mesh layer and In-memory mesh datasets with persistence improvements greatly improves the workflows when using the Mesh Calculator in QGIS. Users can store the intermediate results into virtual layers that are recalculated on the fly (similarly to QGIS expressions for vector layers). The layers can be later persisted to any supported MDAL formats with write capabilities.

Multi identify tool for mesh feature allows to browse the temporal mesh data in more intuitive way and includes the extra information about the Mesh datasets loaded.

These features were sponsored by Artelia Group.
DHI’s dfsu format support
MDAL 0.8.0 supports loading of the external drivers. A first driver, available on Windows QGIS 3.18 only, is popular DFSU format by DHI, which is used to store MIKE 21 output results.

You can see how to configure and use QGIS to work with DFSU format on the DHI’s YouTube channel

Special thanks to the sponsor DHI this feature.
QGIS
We have added the following new features to QGIS to convert between mesh and vector/raster:
- TIN Mesh creation
- Ported most of the processing algorithms from Crayfish to QGIS core
- 3D rendering improvements
- Many Mesh Layer bugfixes
MDAL
- Support for external drivers.
- Fixed HECRAS vector datasets support
- Packaging in conda
Crayfish
- Fixed SAGA flow direction support
- Fixed FFMPEG download link
- Ported most of the processing algorithms from Crayfish to QGIS core
20 January 2021
14:24
Just van den Broecke: That Was 2020
sur Planet OSGeo
Overview of my professional life in 2020. Highlights of living and working in the Open Source Geospatial and OSGeo(.nl|.org)-world in 2020. Organized by “Theme”.

Yes, 2020 was a “special year” in pandemic and political terms. So much has been written and opinions expressed, that I don’t see a need to add to more here. Some of my activities below may make clear how I and we (OSGeo.nl community) dealt.

Screenshots of De Grote Geo Show

TL;DR. My absolute 2020-highlight was initiating and working on De Grote Geo Show (“DGGS, The Big Geo Show”), a live-streamed webshow in “De Wereld Draait Door”-style. This was/is a great team-effort by Dutch Open Source Geo/OSGeo.nl community members. Kudos to Erik, Niene, Mariëlle, Jonna, Willem, Hans and many many more (about 30+ folks)! We streamed 13 shows in 2020 with a huge range of subjects and (international) guests. And we’re still going strong with a DGGS New Year’s Show/Socializer on Sunday, Jan. 24, 2021.

Below follow 2020-highlights by theme.
The Big Geo Show
Let’s start with the absolute 2020 highlight. It became clear in March 2020 that we had to cancel or virtualize our planned (June 2020, ITC Enschede) FOSS4G-NL conference and other OSGeo.nl events. I invested some time into “webinar streaming software”: Zoom, Jitsi, Teams WebEx etc. Those did not really appeal to me for what I had in mind.

StreamYard – Backstage View

For personal streaming, I was testing OBS Studio, streaming to Twitch.tv and YouTube. I then stumbled on “Learn with Jason” . Very entertaining and deeply technical at the same time. I liked the format of having guests in the stream. That triggered me to do something similar with the Open Source Geo community in the Netherlands. Long story short: I had the idea to do a short, 1 hour webshow with various subjects and guests. Before I knew we had a Telegram group of now around 30 folks with a kernel of about 6-8 and started brainstorming. The other element which added to the success was my discovery of StreamYard.com.

Now StreamYard is a whole story by itself. In short it differs from all the well-known video-conf programs that it is a Cloud-based Broadcast Studio. Conceptually it is like making a real TV-show. There is a Stage (main screen) which can be “branded” in all sorts of creative, interactive ways: backgrounds, overlays, banners/tickers, video clips (e.g. for intro/outro or soapboxes), screen layouts, chat messages from viewers. All is controlled by one or more “hosts” (Dutch: “regisseurs”). Guests join “backstage” first and can be added to the “stage” when their item starts. And last but not least: the entire Stage can be streamed to multiple destinations at the same time like YouTube, Twitch.tv, LinkedIn etc. but also to custom RTMP servers. Viewers who missed the show could watch back later. StreamYard works completely in the browser, no installs needed. Ok, StreamYard is not Open Source, but their registration is very minimal: one only needs to provide an email adres to which a temporary login code is sent, no profile-filling etc.

So we just started, all of us not really familiar with providing online events and being first-timers in StreamYard. One learns quickest by just jumping into the deep, learning by doing, not bother about mistakes. We quickly adopted a format for a weekly one-hour show, always at the same time, Thursdays 4-5 PM. A central website tv.osgeo.nl was quickly online (with GitHub and Hugo!), so we can refer to a single, short URL.

The format of each show was basically to have 5-6 items in an hour. An “item” could be an interview with a guest, a technical presentation (or mixed interview/presentation), a column, community updates, a (hands-on) tutorial, a poem, or last but least a live-quiz (via Kahoot) where viewers can participate! After the first show (where we looked a bit like rabbits in headlights!), we quickly realized we need a presenter-host for the entire show. So all subsequent shows had one of us, well mainly Niene, being the talkshow-host. We also decided to do the show in Dutch, except with international guests, as to engage our local communities (OSGeo-NL, QGIS-NL, OpenStreetMap-NL).

Enough theory, show me the show! As said you can watch back all shows via tv.osgeo.nl or directly on the OSGeo.nl YouTube Channel. Hell, you can even binge-watch all episodes !

Many shows were organized around a theme, like 3D (ep. 3 offcourse), Heaven&Outer Space, Corona of the Sun (ep. 6 on Ascension Day). We had some great guests in the show, also international guests, to name a few: Nadieh Bremer and Christian Mayer (ep. 7 – Visualization Special), Hugo Ledoux (TU Delft on 3D), Linda van den Brink (Geonovum) and Tom Kralidis (ep 11 – on OGC API Special), Anita Graser (ep. 9 – QGIS Special), Naomi Bueno de Mesquita and Topi Tjukanov (ep 12. MapTime Special). The last show of 2020, ep. 13 Christmas Edition, was one big PubQuiz.

NewYear on “Grote Geo Eiland”

All in all we did 13 shows in 2020. And 2021? We’ll start with a Newyear Special with OSGeo-NL, QGIS-NL and OpenStreetMap-NL communities. Only the plenary first half-hour in StreamYard, then next a new platform where all can participate and mingle. Just watch and participate on sun, jan 24, 2021, 3 PM (GMT+1) via tv.osgeo.nl.
Geospatial Cloud Services
Moving into providing Geospatial Cloud Services last few years, both as a source of income and to support/strengthen underlying open source projects with which they are developed. Warning: shameless ads below.
- map5.nl is a subscription service for Dutch topographic, historical- and embellished hill-shade and arial maps I started to host in 2015. In 2020 the entire map5.nl server-infrastructure was moved from custom Ubuntu-installs to a complete Ansible/Docker-based setup.
- GeoQoS.com is a hosted GeoHealthCheck (GHC) service on a subscription basis. GHC is an uptime and QoS monitor for (OGC) web services. Customers get their own GHC instance. GeoQoS.com saves the burden of self-hosting GHC. Launch was in 2019, growing steady in 2020. Expect to work more on API and new UI for GeoHealthCheck (below) in 2021.
- Feb 2020 – launched geotoko.nl . geotoko.nl, in short GeoToko, is basically a webshop to download Dutch open geo-datasets. Here one may ask: Dutch geospatial data like Topography, Addresses and Buildings, is already open and publicly available, mainly via Kadaster-PDOK. So why bother reselling? This needs some explanation.
  
  Most Dutch Open geospatial datasets, as available from governmental institutions like Kadaster, are provided in a neutral exchange-format. In practice: GML (Application Schema). Most users are not able to directly digest complex GML in their applications (or don’t want to spend time on that). For over 10 years we run the Open Source project NLExtract to convert these datasets into formats like PostGIS and CSV that can be directly used in applications. NLExtract itself builds on Stetl, an Open Source geospatial ETL-library in Python. NLExtract/Stetl is used a lot, but still will require users to install/maintain an NLExtract/Stetl installation, download datasets, run the ETL, check results etc. That may also be a bridge too far, if e.g. all a customer needs is a one-time CSV of say, all 10 million addresses in The Netherlands. So GeoToko taps into this niche, providing ready-to-use, often enriched Dutch datasets. Pricing is reasonable, compared to other providers, plus organizations and individuals may get highly reduced pricing when they work on Open data applications like OpenStreetMap.
  
  As for development: the GeoToko webshop was developed with Django, Flask/nginx (Download management), Stripe (backend payments) and CKAN. The latter to provide a product-catalogue, sample data and metadata in general, all via GeoCatalogus.nl.
  
  GeoFabriek – Production Chain
  
  Further development concerned automating the NLExtract/Stetl ETL-processes. For this a framework called GeoFabriek, “GeoFactory”, was developed. This automates the entire chain from checking new dataset versions at Kadaster, through downloading, conversions, packing for download and updating the metadata (in GeoCatalogus.nl).
Contract Work
As my focus is more and more on providing “Geospatial Cloud Services” (see above), did not take not too much contract work in 2020, though I am always open for offerings (but may say ‘no’)!
- For Geolicious (Germany). Developed a Wegue interactive map for the German National Park Luneburger Heide. This also gave a boost to the Wegue Open Source project (see below). Was an honour to work together with the great Steve Bennett a.k.a. @stevage.
  
  Various new Widgets for Wegue were developed. To name a few: Enhanced Layer Tree, Enhanced Feature Info (see picture), Share Button, Routing, Multi-language, PDF Print, Download Features, Layout Improvements. Also introduced a “create-app” to have a starter app. We are in the process of merging back these new features into the Wegue core.
Open Source Contributions
Continuous work as a contributor. Apart from some GitLab Projects, you can find/follow me best on GitHub.
- Stetl – Geospatial ETL in Python, maintaining since 2011.
- GeoHealthCheck – Service Status and QoS Checker for OGC Web Services.
- pygeoapi – a Python server implementation of the OGC API suite of standards – joined this great project in 2019. Also in PSC.
- pygeoapi demo server – provided the (auto-)deployment stack for the pygeoapi demo server (code at GitHub). Added COVID-19 NL data provider.
- NLExtract – ETL for Dutch geospatial datasets.
- Wegue – geo-webclient framework based on Vue.js with OpenLayers started by Christian Mayer. Joined this great project in 2019.
- Heron MC – Web Mapping Client based on GeoExt and OpenLayers. Yes, old tech but still in wide use. In time hope to migrate to Wegue (see above). Migrating Heron to Wegue, see first example below.
- MapProxy – joined Program Steering Committee (PSC)
- pg_tileserv Vector Tiles directly from PostGIS!
  Contributed Dockerfile and Docker-example
More Contributions – Handy Docker Images
To support many of the Cloud services and Open Source projects, I develop and maintain handy Docker Images, also available from my DockerHub.
- docker-awstats – AWStats in Docker, oldie, but very effective webstats. Deploy multiple instances in single Docker container. Highly configurable, e.g. also for Traefik access logs.
- docker-jmeter – Apache JMeter wrapped in Docker. Over 1 million pulls!
- docker-mapfish-print – for MapFish Print version 3. MapFish Print allows printing maps as PDFs.
- docker-mapfish-print2 – for MapFish Print version 2 – This version is still used in quite some contexts, at least for Heron and KadViewer.
- docker-rclone – Docker image to perform a rclone sync based on a cron schedule, with healthchecks.io monitoring.
- docker-pgbackup – automated/scheduled PostgreSQL/PostGIS backups for all PostgreSQL-based Docker Containers in its Docker-network. I think it is nifty: just run and forget: your Postgres backups are taken care of. Inspired by: [https:]] .
New Docker images developed in 2020:
- docker-mapserver – Slim Docker Image for MapServer with Li [httpd] FastCGI.
- docker-mapproxy – MapProxy Docker Image adapted, slimmed, from the YAGA Development-Team. Awaiting PR merge.
- docker-mapproxy-mapserver – Docker Image for MapProxy service with built-in MapServer binaries accessed directly (no MapServer service).
- docker-cron – Runs Unix cron, includes docker (compose) client for running remote Docker Images.
OSGeo.nl
As chair of the board, still involved in the OSGeo Dutch Local Chapter, OSGeo.nl since its establishment in 2011.
- Jan 12 – organizer – joint OSGeo.nl and OpenStreetMap NL new-years party at Cafe Dudok in Hilversum. Our last IRL event in 2020….
No FOSS4G-NL, plans are for okt 2021, but hey, 13 episodes of De Grote Geo Show (see above)!
Conferences – Attended
- Feb 20-21 – Vue.js Amsterdam – last physically attended conf in 2020
- Sept 21-22 – GeoPython 2019 – Virtual
- Nov 19 – PostGIS Day 2020 – by crunchydata.com
Had planned: FOSSGIS-DE Freiburg (Germany), FOSS4G-EU Valmiera (Latvia), GeoPython 2020 Bilbao (Spain), FOSS4G 2020 Calgary (Canada) and OSGeo Code Sprint Athens (Greece). Was studying train-tables for all EU events…Oh well.
Talks & Workshops – Provided
Most of my slides on slideshare.net/justb4 . Below my 2020 talks.
- Sept 21-22 – Python Machine Learning & GeoPython 2020 – presented pygeoapi with Francesco Bartoli – [slides HTML]
- Dec 7-9 – GeoNode Summit 2020 – presented pygeoapi with Francesco Bartoli, Tom Kralidis, Angelos Tzotsos – [slides HTML]
- Nov 10 – GeoHealthCheck/geoqos.com workshop for RIVM (National Institute for Public Health and the Environment)
Probably some more, but cannot find back at this moment.
Resolutions 2021
- More effort into Wegue project
- Get back into Kubernetes and containerism in general
- Revive old GeoTracing projects like georambling.com
- HA projects: weather station, wildlife/bird cam, and AQ monitoring with Home Assistant
- More hiking and rambling
- Whatever comes around.
12:30
GeoTools Team: GeoTools 24.2 released
sur Planet OSGeo

The GeoTools team is pleased to share the availability of GeoTools 24.2 :geotools-24.2-bin.zipgeotools-24.2-doc.zipgeotools-24.2-userguide.zipgeotools-24.2-project.zipThis release is published to the osgeo maven repository, and is made in conjunction with GeoServer 2.18.2. This is a stable release and is a recommended upgrade for all users of the GeoTools library.We would like to thank
12:00
Fernando Quadro: PostGIS 3.1 – Suporte de função com precisão fixa
sur Planet OSGeo
Um dos novos recursos que vem no PostGIS 3.1 é o suporte à precisão fixa. Este novo recurso faz parte das novas implementações da biblioteca GEOS 3.9.

Já existem algumas funções que possuem esse novo recurso, são elas: ST_Subdivide, ST_SymDifference, ST_Union e ST_UnaryUnion. O recurso no ST_Union, por exemplo, deve melhorar muitos casos em que as pessoas encontram exceções topológicas. Para uma primeira visão, vamos ver como o suporte de precisão fixa afeta a função ST_Subdivide.

Vou dar uma rápida demonstração de como isso funciona cortando o estado de Massachusetts, que faz parte do arquivo de limites estaduais do censo americano. A geometria original se parece com isto:
```
SELECT geom 
FROM states 
WHERE stusps = 'MA';
```
1. Subdivisão de precisão dupla

Se utilizarmos a maneira antiga, usando o sistema de coordenadas de precisão dupla – isso resultará em 39 linhas numeradas, e será visualizado conforme a imagem abaixo:
```
SELECT f.ord, f.sd_geom
FROM states, 
  ST_Subdivide(states.geom, 300) WITH ORDINALITY AS f(sd_geom,ord) 
WHERE stusps = 'MA';
```
2. Subdivisão de precisão fixa

Agora vamos tentar isso usando uma precisão de escala fixa de 0,001 graus. Como acontece com a maioria das coisas PostGIS, as unidades são conforme o sistema de referência espacial. Neste caso, temos NAD 83 (long/lat), então estamos definindo a precisão fixa em 0,001 graus. Se fosse uma camada do Brasil, com SIRGAS 2000, SAD69, WGS 84 baseadas em coordenadas geográficas (lat/long) também utilizaríamos a mesma lógica em graus. Isso só seria diferente se a informações estivesse em UTM ao invés de Lat/Long, nesse caso ao invés de graus utilizaríamos como unidade de medida, o metro.
```
SELECT f.ord, f.sd_geom
FROM states ,
 ST_Subdivide(states.geom, 300,0.001) WITH ORDINALITY AS f(sd_geom,ord) 
WHERE stusps = 'MA';
```
O resultado é que acabamos com 10 linhas em vez de 39. Você pode perceber que as bordas são um pouco mais suaves do que a imagem anterior. Isso ocorre porque em uma precisão fixa, quando a geometria é sobreposta na grade fixa, os pontos menores do que o tamanho da grade se tornam um, resultando em menos pontos, portanto, maior extensão da área antes de atingir um limite de 300 pontos subdivididos e também uma imagem mais uniforme.

Conforme você aumenta o tamanho da grade, você obtém um resultado mais “pixelado”. Aqui aumentamos nosso tamanho de grade para 0,1 grau e acabamos com a fidelidade do PacMan. O resultado são 2 linhas de geometrias muito “pixeladas”, veja:
```
SELECT f.ord, f.sd_geom
FROM states, 
 ST_Subdivide(states.geom, 300,0.1) WITH ORDINALITY AS f(sd_geom,ord) 
WHERE stusps = 'MA';
```
Este post foi escrito originalmente por Regina Obe e foi traduzido e adaptado livremente por este blog.

Fonte: Boston GIS
1:00
GeoServer Team: GeoServer 2.18.2 Released
sur Planet OSGeo
We are pleased to announce the release of GeoServer 2.18.2 with downloads (war bin), documentation and extensions.

This release is made in conjunction with GeoTools 24.2 and GeoWebCache 1.18.2. This is a stable release recommended for production systems.

Thanks to everyone who contributed, and Alessandro Parma, Andrea Aime (GeoSolutions) for making this release.
Improvements and Fixes
This release includes a number of fixes in core and extensions:
- Improved GWC seeding scalability (also check GWC 1.18.2 release notes).
- Fixed back-links generation during WPS asynchronous requests, when a proxy base URL is used.
- Fixed a GeoFence server packaging issue.
- A number of dependent libraries have been upgraded, including the PostgreSQL and MySQL JDBC drivers, HTTP components, Guava.
Community Updates
Things get really interesting when looking at functionality provided by community modules:
- The new support for COGs based on ImageIO Ext landed in a community module, adding it adds support for COG in both the GeoTIFF reader and image mosaic. Support for harvesting COG granules is also added in the REST API.
- A deadlock in JDBCConfig has been resolved, along with issues related to high load when GeoServer has just started up.
- The WPS download “map” and “animation” processes sport improved legend support.
For more information check the 2.18.2 release notes.
About GeoServer 2.18
Additional information on GeoServer 2.18 series:
- State of GeoServer 2.18 (slides)
- GeoServer Orientation (slides|video)
- Release Notes ( 2.18.2 | 2.18.1 | 2.18.0 | 2.18-RC)
1:00
EOX' blog: Earth Observation Data Cubes as a Service
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Editor’s Note: The information in this blog post is part of Stefan’s master’s thesis, that he wrote as an intern at EOX. Have you wondered how to extract meaningful information from loads of satellite imagery spanning several months? You are not alone! Satellite imagery is considered Big Earth Data ...
19 January 2021
0:26
Narcélio de Sá: TecnoloGEO 26 – Entrevista o Fascinante Mundo do Sensoriamento Remoto
sur Planet OSGeo
Neste podcast: No TecnoloGEO 26, PRIMEIRO episódio de entrevista do TecnoloGEO, tivemos a honra de receber o incrível Gustavo Baptista que veio nos falar um pouco sobre o quão incrível é fascinante o mundo do Sensoriamento Remoto.

E nesse episódio o Prof. Gustavo vem nos explicar como e porque criou o podcast, algumas aventuras da vida de cientista no Brasil, contou histórias de bastidores sensacionais sobre o histórico do Sensoriamento Remoto e muito mais!
Aperte o ? para começar o episódio
O TecnoloGEO é um podcast encabeçado por Murilo Cardoso e Narcélio de Sá. O Podcast é mais uma iniciativa que visa informar, instruir e, porque não, divertir pessoas que se interessam pela área de Geotecnologias.
Referências e Indicações do TecnoloGEO 26:
Sugestões de literatura:
Sugestões de vídeos:
- Geospatial Revolution
Nota de transparência: os links dos livros acima têm código de afiliado. Clicando neles, os preços não mudam, mas eu posso ganhar uma comissão da Amazon.

Fale conosco! E não esqueça de deixar o seu comentário na postagem desse episódio!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:
Narcélio de SáLeonardo
João AtaídeMichelangelo
Murilo CardosoRaphael
Tiago SouzaDonatello
Luis SadeckMestre Splinter
The post TecnoloGEO 26 – Entrevista o Fascinante Mundo do Sensoriamento Remoto appeared first on Narcélio de Sá.
18 January 2021
17:19
GeoSolutions: Webinar: State of GeoServer Q1 202, 2.18 and beyond
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12:00
Fernando Quadro: PostGIS 3.1 – Geradores de grades
sur Planet OSGeo
No último mês, foi lançada oficialmente a versão 3.1 do PostGIS, extensão espacial do PostgreSQL e com ela algumas novidades como a que iremos ver neste post, que são os geradores de grades.

Resumir dados em uma grade fixa é uma maneira comum de preparar dados para análise. As grades fixas têm algumas vantagens sobre os limites naturais e administrativos:
- Sem apelo às autoridades superiores
- Áreas de unidade iguais
- Distâncias iguais entre as células
- Bom para passar dados do domínio computacional “espacial” para um domínio “não espacial”
Idealmente, queremos ser capazes de gerar grades que tenham alguns recursos-chave:
- Ponto de origem fixo, de modo que a grade pode ser gerada novamente e não se mover
- Coordenadas de células fixas para um determinado tamanho de célula, de modo que a mesma célula possa ser referida apenas usando um endereço de célula, sem ter que materializar os limites da célula
A função ST_SquareGrid (tamanho, limites) gera uma grade com uma origem em (0, 0) no plano de coordenadas e preenche os limites dos quadrados da geometria fornecida.
```
SELECT (ST_SquareGrid(400000, ST_Transform(a.geom, 3857))).* 
FROM admin a  
WHERE name = 'Brazil';
```
Portanto, uma grade gerada usando a geometria do Brasil como referência tem esta aparência.

A função ST_HexagonGrid (tamanho, limites) funciona da mesma forma que a função de grade quadrada.

Os hexágonos são populares para alguns fins de exibição cartográfica e de modelagem. Surpreendentemente, eles também podem ser indexados usando o mesmo esquema de indexação bidimensional dos quadrados.

A grade do hexágono começa com um hexágono (0, 0) centralizado na origem, e a grade para os limites incluem todos os hexágonos que tocam os limites.

Tal como acontece com a grade quadrada, as coordenadas dos hexágonos são fixas para um tamanho de grade específico.
```
SELECT (ST_HexagonGrid(100000, ST_Transform(a.geom, 3857))).* 
FROM admin a  
WHERE name = 'Germany';
```
Aqui está uma grade hexagonal de 100 km da Alemanha:

É possível materializar resumos baseados em grade, sem realmente materializar as grades, usando as funções do gerador para criar as grades desejadas em tempo real.

Aqui está um resumo dos pontos de população, usando uma grade hexadecimal:
```
SELECT sum(pop_max) as pop_max, hexes.geom
FROM
    ST_HexagonGrid(
        4.0,
        ST_SetSRID(ST_EstimatedExtent('places', 'geom'), 4326)
    ) AS hexes
    INNER JOIN
    places AS p
    ON ST_Intersects(p.geom, hexes.geom)
GROUP BY hexes.geom;
```
Também é possível unir grades dinâmicas a ferramentas de visualização, para experiências de usuário mais dinâmicas, basta você adicionar essas visões ao seu GeoServer, por exemplo.

Este post foi escrito orignalmente por Paul Ramsey em inglês e foi traduzido e adaptado livremente por este blog.

Fonte: Crunchy Blog
10:31
Jackie Ng: Announcing: vscode-map-preview 0.5.8
sur Planet OSGeo

This minor update fixes display of spatial data that spans the international date line by turning off the flag that causes OpenLayers to auto-wrap such features
4:39
Tyler Mitchell: DIY Battery – Weekend Project – Aluminum + Bleach?
sur Planet OSGeo

The post DIY Battery – Weekend Project – Aluminum + Bleach? appeared first on spatialguru.com.
15 January 2021
11:28
Andrea Antonello: SMASH 1.6.3 is out - experimental Postgis
sur Planet OSGeo

We just released SMASH 1.6.3 on the stores. It should be available within the day.
This is basically a bugfix release, but we decided to also release one feature that will be very important in the future: postgis support.
The biggest issue with the last release is that we introduced an error trapping engine to allow the user to better send issues of the application to the developers. The problem we didn't notice for soem obscure reason is that everytime an online map tile would not be properly downloaded, this would result into a crash report. Your many email explained well the situation :-))) This version fixes this.
We also enhanced the WMS source creator (which still needs much love) with the possibility to select the image format:

The note taking workflow just got a bit simpler. You can just tap on the text and the edit field is empty if the note had never been edited. This allows for less taps, which is great out in the field.
Last but not least, we started adding PostGIS support. This will be a 2 steps effort. Step one (this one) is to add full online support. This means that you now can access the remote db view from the layers list:
Let's assume you have a test db running in your network, something like:
Then you can add a new remote db as:
you can enter the table or trigger a refresh, which will also help you in testing the connection:
you can also optionally add a where condition, if you need to load only a subset of the data.
Once the db is completed, it will stay in your list of available db datasets. Just add it as a layer to teh map using the rightmost button:
which will result in:
and on the map:
well, this looks kind of ugly. You might have seen in some previous post that SMASH supports styling via SLD specification. Well, adding style the same as we do for geopackage brings us to this:
Nice right?
Now this is a vector layer that can be selected:
The above view allows also for table data editing.
And also geometric editing is allowed:
So, why is this the first of two steps? Well, out in the field we often do not have network access, so we will need to create a solution that would allow to create a local geopackage cache, which then eventually can sync back to the remote db.
But we decided to first release the direct online postgis access, since that still has some nice usecases in situations in which the network is available. Once we have this one working stable, we will move on to the next step. So, if you are into Postgis and want to help, please test and report issues.
Enjoy!
14 January 2021
2:07
Martin Davis: Using the GEOS geosop CLI
sur Planet OSGeo
In a previous post I announced the new geosop command-line interface (CLI) for the GEOS geometry API. This post provides examples of using geosop for various tasks. (Refer to the README for more information about the various options used.)
API Testinggeosop makes testing the GEOS library much easier. Previously, testing the behaviour of the API usually required breaking out the C compiler (and updating autotools and cmake build files, and deciding whether to commit the new code for later use or throw it away, etc, etc). Now testing is often just a matter of invoking a geosop operation on appropriate data, or at worst adding a few lines of code to the exiting framework.
For example, there is a long-standing issue with how GEOS handles number formatting in WKT output. There are recent bug reports about this in GeoSwift and the Julia LibGEOS.  geosop makes it easy to run the test cases and see the less-than-desirable output:
geosop -a "POINT (654321.12 0.12)" -f wktPOINT (654321.1199999999953434 0.1200000000000000)
geosop -a "POINT (-0.4225977234 46.3406448)" -f wktPOINT (-0.4225977234000000 46.3406447999999997)
There's also an issue with precision handling. To test this we added a --precision parameter to geosop. (This is the kind of rapid development enabled by having the CLI codebase co-resident with the API.)
geosop -a "POINT (654321.126 0.126)" --precision 2 -f wktPOINT (6.5e+05 0.13)
Again we see undesirable behaviour. Using scientific notation for small numbers is unnecessary and difficult to read. And the precision value is determining the number of significant digits, not the number of decimal places as intended by the GEOS WKTWriter.setRoundingPrecision API.
These were all caused by using standard C/C++ numeric formatting, which is surprisingly limited and non-useful. After some fine work by Paul Ramsey to integrate the much better Ryu library, GEOS now has WKT output that is sensible and handles precision in a useful way.
By default, WKTWriter now nicely round-trips WKT text:
geosop -a "POINT (654321.12 0.12)" -f wktPOINT (654321.12 0.12)
geosop -a "POINT (-0.4225977234 46.3406448)" -f wktPOINT (-0.4225977234 46.3406448)
If WKTWriter.setRoundingPrecision or GEOSWKTWriter_setRoundingPrecision is called, the precision value applies to the decimal part of the number:
geosop -a "POINT (654321.1234567 0.126)" --precision 0 -f wktPOINT (654321 0)
geosop -a "POINT (654321.1234567 0.126)" --precision 2 -f wktPOINT (654321.12 0.13)
geosop -a "POINT (654321.1234567 0.126)" --precision 4 -f wktPOINT (654321.1235 0.126)
geosop -a "POINT (654321.1234567 0.126)" --precision 6 -f wktPOINT (654321.123457 0.126)
Performance TestingA key use case for geosop is to provide easy performance testing. Performance of geometric operations is highly data-dependent. It's useful to be able to run operations over different datasets and measure performance. This allows detecting performance hotspots, and confirming the efficiency of algorithms.
As a simple example of performance testing, for many years GEOS has provided optimized spatial predicates using the concept of a prepared geometry. Prepared geometry uses cached spatial indexes to dramatically improve performance for repeated spatial operations against a geometry. Here is a performance comparison of the intersects spatial predicate in its basic and prepared form.
geosop -a world.wkt -b world.wkt -t intersectsRan 59,536 intersects ops ( 179,072,088 vertices) -- 16,726,188 usec (GEOS 3.10.0dev)
geosop -a world.wkt -b world.wkt -t intersectsPrepRan 59,536 intersectsPrep ops ( 179,072,088 vertices) -- 1,278,348 usec (GEOS 3.10.0dev)
The example of testing the world countries dataset against itself is artificial, but it shows off the dramatic 16x performance boost provided by using a prepared operation.
Another interesting use is longitudinal testing of GEOS performance across different versions of the library. For instance, here's a comparison of the performance of interiorPoint between GEOS 3.7 and 3.8. The interiorPoint algorithm in GEOS 3.7 relied on overlay operations, which made it slow and sensitive to geometry invalidity. GEOS 3.8 included a major improvement which greatly improved the performance, and made the algorithm more robust. Note that the dataset being used contains some (mildly) invalid geometries towards its end, which produces an error in GEOS 3.7 if the entire dataset is run. The  --alimit 3800 option limits the number of geometries processed to avoid this issue.
GEOS 3.7geosop -a ne_10m_admin_1_states_provinces.wkb --alimit 3800 -t interiorPointRan 3,800 operations ( 1,154,703 vertices) -- 2,452,540 usec (GEOS 3.7)
GEOS 3.8geosop -a ne_10m_admin_1_states_provinces.wkb --alimit 3800 -t interiorPointRan 3,800 operations ( 1,154,703 vertices) -- 35,665 usec (GEOS 3.8)
The dramatic improvement in interiorPoint performance is clearly visible.
GeoprocessingThe raison d'etre of GEOS is to carry out geoprocessing. Most users will likely do this using one of the numerous languages, applications and databases that include GEOS. But it is still useful, convenient, and perhaps more performant to be able to process geometric data natively in GEOS.
The design of geosop enables more complex geoprocessing via chaining operations together using shell pipes. For example, here is a process which creates a Voronoi diagram of some points located in the British Isles, and then clips the Voronoi polygons to the outline of the islands. This also shows a few more capabilities of geosop:
- input can be supplied as WKT (or WKB) geometry literals on the command-line
- input can be read from the standard input (here as WKB)
- the output data is sent to the standard output, so can be directed into a file
geosop -a "MULTIPOINT ((1342 1227.5), (1312 1246.5), (1330 1270), (1316.5 1306.5), (1301 1323), (1298.5 1356), (1247.5 1288.5), (1237 1260))" -f wkb voronoi | geosop -a stdin.wkb -b uk.wkt -f wkt intersection > uk-vor.wkt
13 January 2021
2:31
Martin Davis: Introducing geosop - a CLI for GEOS
sur Planet OSGeo
The GEOS geometry API is used by many, many projects to do their heavy geometric lifting. But GEOS has always had a bit of a PR problem. Most of those projects provide a more accessible interface to perform GEOS operations. Some offer a high-level language like Python, R, or SQL (and these typically come with a REPL to make things even easier). Or there are GUIs like QGIS, or a command-line interface (CLI) like GDAL/OGR.
But you can't do much with GEOS on its own. It is a C/C++ library, and to use it you need to break out the compiler and start cutting code. It's essentially "headless". Even for GEOS developers, writing an entire C program just to try out a geometry operation on a dataset is painful, to say the least.
There is the GEOS XMLTester utility, of course. It processes carefully structured XML files, but that is hardly convenient. (And in case this brings to mind a snide comment like "2001 called and wants its file format back", XML actually works very well in JTS and GEOS as a portable and readable format for geometry tests. But I digress.)
JTS (on which GEOS is based) has the TestBuilder GUI, which works well for testing out and visualizing the results of JTS operations. JTS also has a CLI called JtsOp. Writing a GUI for GEOS would be a tall order. But a command-line interface (CLI) is much simpler to code, and has significant utility. In fact there is an interesting project called geos-cli that provides a simple CLI for GEOS. But it's ideal to have the CLI code as part of the GEOS project, since it ensures being up-to-date with the library code, and makes it easy to add operations to test new functionality.
This need has led to the development of geosop. It is a CLI for GEOS which performs a range of useful tasks:
- Run GEOS operations to confirm their semantics
- Test the behaviour of GEOS on specific geometric data
- Time the performance of operation execution
- Profile GEOS code to find hotspots
- Check memory usage characteristics of GEOS code
- Generate spatial data for use in visualization or testing
- Convert datasets between WKT and WKB
geosop has the following capabilities:
- Read WKT and WKB from files, standard input, or command-line literals
- Execute GEOS operations on the list(s) of input geometries. Binary operations are executed on every pair of input geometries (i.e. the cross join aka Cartesian product)
- Output geometry results in WKT or WKB (or text, for non-geometric results)
- Display the execution time of data input and operations
- Display a full log of the command processing
Here's a look at how it works.
geosop -h gives a list of the options and operations available:
geosop - GEOS v. 3.10.0devExecutes GEOS geometry operationsUsage: geosop [OPTION...] opName opArg
-a arg source for A geometries (WKT, WKB, file, stdin, stdin.wkb) -b arg source for B geometries (WKT, WKB, file, stdin, stdin.wkb) --alimit arg Limit number of A geometries read -c, --collect Collect input into single geometry -e, --explode Explode result -f, --format arg Output format -h, --help Print help -p, --precision arg Sets number of decimal places in WKT output -r, --repeat arg Repeat operation N times -t, --time Print execution time -v, --verbose Verbose output
Operations: area A - computes area for geometry A boundary A - computes boundary for geometry A buffer A N - cmputes the buffer of geometry A centroid A - computes centroid for geometry A contains A B - tests if geometry A contains geometry B containsPrep A B - tests if geometry A contains geometry B, using PreparedGeometry containsProperlyPrep A B - tests if geometry A properly contains geometry B using PreparedGeometry convexHull A - computes convexHull for geometry A copy A - computes copy for geometry A covers A B - tests if geometry A covers geometry B coversPrep A B - tests if geometry A covers geometry B using PreparedGeometry difference A B - computes difference of geometry A from B differenceSR A B - computes difference of geometry A from B rounding to a precision scale factor distance A B - computes distance between geometry A and B distancePrep A B - computes distance between geometry A and B using PreparedGeometry envelope A - computes envelope for geometry A interiorPoint A - computes interiorPoint for geometry A intersection A B - computes intersection of geometry A and B intersectionSR A B - computes intersection of geometry A and B intersects A B - tests if geometry A and B intersect intersectsPrep A B - tests if geometry A intersects B using PreparedGeometry isValid A - tests if geometry A is valid length A - computes length for geometry A makeValid A - computes makeValid for geometry A nearestPoints A B - computes nearest points of geometry A and B nearestPointsPrep A B - computes nearest points of geometry A and B using PreparedGeometry polygonize A - computes polygonize for geometry A reducePrecision A N - reduces precision of geometry to a precision scale factor relate A B - computes DE-9IM matrix for geometry A and B symDifference A B - computes symmetric difference of geometry A and B symDifferenceSR A B - computes symmetric difference of geometry A and B unaryUnion A - computes unaryUnion for geometry A union A B - computes union of geometry A and B unionSR A B - computes union of geometry A and B
Most GEOS operations are provided, and the list will be completed soon.
Some examples of using geosop are below.
- Compute the interior point for each country in a world polygons dataset, and output them as WKT:
geosop -a world.wkt -f wkt interiorPoint
- Determine the time required to compute buffers of distance 1 for each country in the world:
geosop -a world.wkt --time buffer 1
- Compute the union of all countries in Europe:
geosop -a europe.wkb --collect -f wkb unaryUnion

The README gives many more examples of how to use the various command-line options. In a subsequent post I'll give some demonstrations of using geosop for various tasks including GEOS testing, performance tuning, and geoprocessing.
Future WorkThere's potential to make geosop even more useful:
- GeoJSON is a popular format for use in spatial toolchains. Adding GeoJSON reading and writing would allow geosop to be more widely used for geo-processing.
- Adding SVG output would provide a way to visualize the results of GEOS operations.
- Improve support for performance testing by adding operations to generate various kinds of standard test datasets (such as point grids, polygon grids, and random point fields).
And of course, work will be ongoing to keep geosop up-to-date as new operations and functionality are added to GEOS.
12 January 2021
10:00
CARTO Blog: How to Use Spatial Data to Identify CPG Demand Hotspots
sur Planet OSGeo

In recent years, the consumption and promotion of products which fall under the category of Organic / Natural / Local has increased dramatically. These are specific types o...
11 January 2021
1:00
Lutra consulting: Mergin and PostGIS in action
sur Planet OSGeo
One of the challenges of data collection projects is consolidating all the data a central database, such as Postgres/PostGIS. Using PostGIS as a live survey layer is not usually recommended:
- Security: exposing your database to the wider internet
- Access and connectivity: internet connection is not always guaranteed for field survey projects
A more cumbersome way around this, is to convert your tables from PostGIS to a file based GIS layer (e.g. GeoPackage) and take the files with you to the field. This will create a new problem: keeping all the tables (from multiple surveyors) and the PostGIS table up-to-date.

During a survey to assess water access for villages in Limpopo province, South Africa, our friends at Kartoza have commissioned us to extend the Mergin service to support PostGIS. The Mergin service already supports file-based data synchronisation. The aim was to bridge the gap between the Mergin service and PostGIS so that the changes from Mergin immediately appear on PostGIS and vice versa.

To facilitate that, we further developed the Geodiff library to support Postgres driver. In addition, we developed mergin-db-sync tool to sync the tables from Postgres database with the Mergin service. The mergin-db-sync tool runs as a service (daemon) that keeps an eye on a particular Mergin project, and if there is a new version of the project, it will fetch the most recent changes and apply them to database tables in PostgreSQL. It works also the other way around at the same time: it looks for changes in the configured PostgreSQL schema and upload them in a new version of the Mergin project if any changes were detected. This service can be easily started on the local Intranet (where the PostgreSQL database is run) and therefore it does not need any adjustments to the firewall to allow access to the local database from public Internet.

The above diagram details how Postgres/PostGIS synchronisation works with the Mergin service via the DB-Sync tool.
- Tables 1 and 2 from the Postgres/PostGIS server are set up to work with the Mergin service
- DB-Sync tool runs on a server on a regular interval to sync with the Mergin service
- An offline version of Tables 1 and 2 are provided within the QGIS survey project on Mergin
- Several surveyors download the project and add their data mostly while offline. The data are then synced with the Mergin.
From the surveyors’ point of view, the extra set up to sync with the Postgres/PostGIS does not affect their workflow. In fact, mergin-db-sync tool acts as another client syncing data to the Mergin project, therefore it is possible to see all the changes in the project log originated from mergin-db-sync tool.

The tool is available on GitHub with a permissive open source license (MIT). At this point it supports PostgreSQL, but the mechanism is fairly generic and support for other database engines may be added in the future without great effort. All the heavy lifting is done by the Geodiff library which has been significantly enhanced during the development of mergin-db-sync tool.

To try the tool, please follow the instructions on the project’s readme on GitHub. The easiest way is to use it in a Docker container.

If you have any issues or feedback to enhance the tool, you can file a ticket on the project repo.

If you’d like to set up DB-Sync tool with your Mergin survey projects, you can contact us on info@lutraconsulting.co.uk
10 January 2021
14:33
From GIS to Remote Sensing: Major Update: Semi-Automatic Classification Plugin v. 7.4.0
sur Planet OSGeo

This post is about a new update of the Semi-Automatic Classification Plugin v. 7.4.0.
This is mainly a maintenance version with the objective to solve the multiprocessing issues related to Python installation especially in Mac OS.
In the tab Processing setting, it is now possible to enter the Python executable path (e.g. /usr/local/bin/python3) and the path to the GDAL directory containing tools such as gdal_translate and gdalwarp (e.g. /usr/bin).Of course, these paths can vary according to the operating system and the program installation.

Also, in the tab Debug it is possible to test the dependencies and check the cause of the errors in the log file.Hopefully these update should solve most of the issues related to SCP installation in Mac OS.If you are having issue related to SCP installation and multiprocessing, please report it at [https:]] , also testing the dependencies and attaching the log file as described here.
For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin.
08 January 2021
12:00
MapTiler: Mapbox GL open-source fork
sur Planet OSGeo

After Mapbox announced the closure of Mapbox GL JS, their JavaScript library for displaying maps using WebGL, the community made a collective decision to maintain and further develop the last open-source version and build a free alternative. Read the story and technical details.
MapLibre is born
In December 2020, Mapbox released the second version of their JavaScript library for publishing maps online. However, this time all the new features were overshadowed by a change in the license: previously free as a freedom, it became closed for external contributors and usage was restricted to people with active Mapbox subscriptions. One have to pay even for loading this JavaScript library.

The community reacted swiftly: forks of the latest open-source version were made almost immediately by multiple parties. In another positive development, the community came together the next day and agreed to make this a joint effort, rather than splitting energies. A video call was organized and the MapLibre coalition was formed. It includes people working for MapTiler, Elastic, StadiaMaps, Microsoft, Ceres Imaging, WhereGroup, Jawg, Stamen Design, etc.

The name, MapLibre, stands as an abbreviation for Map library restarted (or reinvented), at the same moment the word Libre refers to freedom & independence.

Members of open-source communities and companies working in the map industry agreed on basic rules, which were formalized into a guiding Memorandum. This summarized the project goals, rough roadmap, community governance details, and communication channels. Read the full text of the memorandum and join the effort: new members are welcomed!
Independent JavaScript GL mapping library
MapLibre is based on Mapbox GL JS 1.13, the most recent version available under the BSD license. The source code and ticketing system is hosted on GitHub. Together with the fork, the whole ecosystem around the library (NPM packages, binding to react, …) were also updated.

For a complete working example of the map with a style loaded from a CDN (JavaScript and HTML code sample), click on the button below (note, MapLibre is still under heavy development - this is the fourth release candidate). However, MapLibre will always be provider-independent, and you can load maps from your preferred provider or self-hosted maps.

JavaScript tutorial

Current Mapbox GL JS users can simply switch by changing a few lines of code.

If you use npm and depend on mapbox-gl directly, simply replace mapbox-gl with maplibre-gl in package.json:

You can use this library in your React app as well with help of React.useRef and React.useEffect hooks. See our live example. The most popular bindings for React and other libraries are going to be updated soon.
Native SDK for Android & iOS
As development of open-source mobile map SDKs for Android & iOS was discontinued somewhere in the middle of last year, the MapTiler team was for some time working internally on own version. This latest situation sped things up, and we are releasing the code for a broad community.

MapLibre GL Native was forked from mapbox-gl-native prior to their switch to a non-OSS license. The fork also includes Maps SDK for iOS and macOS (forked from mapbox-gl-native-ios) and Android SDK (forked from mapbox-gl-native-android). The first version is available on GitHub.

The source code which was updated to the latest version of Clang/Xcode and can be used immediately. Continuous integration and delivery was moved over to GitHub Actions from CircleCI and iOS binaries are distributed as a Swift package that contains binaries packaged using the new XCFramework format.

Android tutorial iOS tutorial

Our further proposal is to move this fork under the wings of MapLibre and further develop it together with the community. Suggestions for improvements and pull requests are welcomed!

Even though Mapbox has changed the direction in relation to open-source, we would like to express a huge appreciation to the great team of Mapbox engineers for all their effort made on the development of the numerous tools and components used in here.

Update 5.2.2021: as we promised in January, the Native SDKs for Android and iOS has been moved under the MapLibre organization on GitHub.
06 January 2021
11:58
Andrea Antonello: Hortonmachine 0.10.0 released - get a grip on your basins size
sur Planet OSGeo

It sure is a while that I don't post about the Hortonmachine. While we work a lot on it and with it, it always kind of stays in background. Well today we decided to make a new release due to some tools that will be used in production environments. And I think one is definitely worth to mention.
Some of you might have used the netnumbering module to extract subbasins on a network. The module also accepts monitoring points to allow splitting the basin in fixed points.
For a watershed like this:
The result is something like:
Now, some models, as for example Riccardo Rigon's Geoframe, need a bit more control over the basin's size. This is why, on Riccardo's request, we added the possibility to set a desired output basins size and buffer threshold, to handle some degree of flexibility.
It has been quite some fun and recursive exercise to aggregate in the best possible way basins that are too small. We added plantuml mindmap outputs to be able to better check the way the hierarchy is getting merged. This has been really helpful, in particular to check large basins and also to check on monitoring point positions, which is where basins are meant to keep a fixed border.
Here the mindmap of the above basin, with references to areas and fixed basins:

and finally the aggregated basins mindmap assuming a desired basin size of 100000 cells (10*10 meters here) and a 20% of threshold:

and the resulting subbasins map:
The result is quite nifty, given the fact that the only basins that do not obey to the 20% buffer are either basins that are fixed and at the very top (so they can't be merged with anyone) or basin 6 which is "blocked" between 2 fixed basins, so same as before, no way to enlarge it by aggregation.
Well, you can find a release containing the tools at the usual release download site of the Hortonmachine here.
Enjoy! We surely did :-)
04 January 2021
4:00
Narcélio de Sá: TecnoloGEO #25 – Resoluções de Ano novo
sur Planet OSGeo
Neste podcast: Tchau, 2020! Feliz 2021! No TecnoloGEO 25, Narcélio de Sá, Murilo Cardoso, Luiz Sadeck, João Ataíde e Tiago Sousa apresentam suas resoluções para 2021, e iremos guardar essa lista para o nosso último episódio de 2021, uma retrospectiva do ano, onde iremos compartilhar se conseguimos, ou não, obter sucesso em nossas metas do ano.
Aperte o ? para começar o TecnoloGEO 25
O TecnoloGEO é um podcast encabeçado por Murilo Cardoso e Narcélio de Sá. O Podcast é mais uma iniciativa que visa informar, instruir e, porque não, divertir pessoas que se interessam pela área de Geotecnologias.
Referências e Indicações do TecnoloGEO 25:
Sugestões de literatura:
- Happy City: Transforming Our Lives Through Urban Design – Charles Montgomery
- A invenção da Natureza – Andrea Wulf
- A origem das espécies – Charles Darwin
- Admirável mundo novo – Aldous Leonard Huxley
- 1984 – George Orwell
- O futuro da mente – Michio Kaku
Sugestões audiovisual:
Fale conosco! E não esqueça de deixar o seu comentário na postagem desse episódio!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:
Narcélio de SáLeonardo
João AtaídeMichelangelo
Murilo CardosoRaphael
Tiago SouzaDonatello
Luis SadeckMestre Splinter
The post TecnoloGEO 25 – Resoluções de Ano novo appeared first on Narcélio de Sá.
01 January 2021
18:41
Tom Kralidis: Bye Bye 2020
sur Planet OSGeo

So 2020 didn’t quite work out as expected or hoped. Still, in a year where days seemed to fold into one another, the 24 hour home office and endless virtual meetings, some successes: pygeoapi: the Python OGC API server continues to pick up steam. The project was presented, demoed and discussed at numerous events. 2020 […]
8:31
From GIS to Remote Sensing: Very Happy New Year by From GIS to Remote Sensing
sur Planet OSGeo
This post is to wish you all a very happy new year!

The new version 7 of the Semi-Automatic Classification Plugin (SCP), for QGIS 3, has been recently released. I'm really grateful to all the people who have contributed to this project, through their work translating the interface and the user manual, fixing bugs and reporting issues, and donating.
In 2021, I'll continue the development of the SCP, focusing on new features for automating and improving the processing of remote sensing images, in particular:
- improving the batch tools for automating processes;
- development of standalone APIs to use the tools through the command line or Python;
- improvement of the user interface.
And of course I'll write new tutorials about applications and case studies, such as image clustering classification of Sentinel-1, Random Forest classification, and much more.

For any comment or question, join the Facebook group about the Semi-Automatic Classification Plugin.
1:05
Stefano Costa: Peer Community in Archaeology, una peer review migliore per tutti
sur Planet OSGeo
Qualche giorno fa ho completato la mia prima peer review per Peer Community in Archaeology. Faccio peer review (o referaggio, come molti dicono in anglo-italico) da una decina d’anni. Per diversi anni sono stato editor del Journal of Open Archaeology Data e ho gestito il processo di peer review, che può essere anche molto estenuante e sempre, rigorosamente, gratuito.

Quando ho scoperto PCI Archaeology mi è parsa subito una iniziativa con grande potenzialità. La open peer review non è una novità, ma con PCI viene messo a sistema l’utilizzo sistematico dei preprint, che sono pressoché sconosciuti tra gli archeologi e ancora più tra gli archeologi italiani. Tutto il progetto Peer Community in conta al momento 11 comunità specializzate in discipline varie.

PCI Archaeology è attivo dal 2019, ha un cuore francese, è sostenuto dal CNRS e da molte università, ma anche dal Max Planck Institute, se avete bisogno di farvi convincere dal prestigio. C’è già una lista importante di riviste che ha aderito al progetto, tra cui Quaternary, PLOS One, Internet Archaeology, Open Quaternary e ovviamente Journal of Open Archaeology Data. Tra gli 80 recommenders ci sono diversi italiani, con una maggioranza sul versante preistorico e scientifico. Chi fa parte del comitato editoriale di una rivista dovrebbe dedicare un po’ di tempo a capire come funziona PCI, la via più semplice è candidarsi come reviewer e sperimentare di persona il funzionamento.
Perché la peer review aperta di PCI Archaeology è migliore per tutti?
Per gli autori, consente di far circolare la propria ricerca appena pronta per l’invio a una rivista e di sottoporla a un processo trasparente. Il preprint deve essere caricato su un server esterno affidabile, come OSF Preprints o Zenodo, perché la review ha una sua autonomia editoriale. È possibile per i reviewer rimanere anonimi ma nella maggior parte dei casi ci sarà un nome affiancato alla review che riceviamo. Il ruolo del “recommender” è abbastanza originale ed è un po’ come avere un editor su misura per ciascun articolo, che può anche essere suggerito tra quelli attivi al momento (oggi sono 80). Quando la review finisce, il preprint diventa una porzione di un “oggetto editoriale” più esteso che collega le review, la recommendation finale (che può anche essere negativa!) e le risposte degli autori alle review. Tutto è pubblico e pubblicato, archiviato e citabile, e il preprint può a questo punto essere inviato a una rivista tradizionale oppure anche citato così com’è, perché di fatto ha tutte le caratteristiche di un articolo completo: identificativo permanente, archiviazione a lungo termine, peer review.

Per i reviewer, anzitutto la trasparenza incoraggia a svolgere con maggiore attenzione la revisione, perché tutti potranno leggere i nostri commenti ? anche se scegliessimo di rimanere anonimi quella review è comunque opera nostra. Ma l’aspetto più incredibile è la possibilità di leggere gli altri reviewer! L’articolo che ho rivisto ha avuto ben 4 reviewer, è stato incoraggiante vedere che diversi punti delle nostre review erano molto simili ed è ancor più stimolante invece capire quali aspetti mi erano sfuggiti, in che modo posso migliorare la mia comprensione di un articolo e la mia attività futura di ricerca e pubblicazione.

Per i lettori, credo che una diffusione dei preprint in archeologia possa solo aiutare a far crescere la ricerca, e rendere più brillante la ricerca di buona qualità. Ovviamente i preprint sono una forma di open access, quindi tra i vantaggi c’è anche quello di scansare costosi abbonamenti che ormai nemmeno più le biblioteche specializzate riescono a mantenere. Leggere in dettaglio i commenti fatti da altri ad un articolo è corroborante, per me stimola immediatamente un approccio di curiosità, approfondimento e dibattito. Ovviamente le discussioni avvengono comunque, ma si perdono nell’etere. E comunque il fatto che un articolo venga presentato alla comunità scientifica da una persona terza è una tradizione con radici profonde, che solo la burocratizzazione dell’editoria accademica ha cancellato.

Allora, la breve lista di suggerimenti per iniziare il 2021:
- iscriversi come reviewer a PCI Archaeology
- leggere gli articoli già raccomandati!
- per la prossima pubblicazione, caricare il preprint e mandarlo a PCI Archaeology

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