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• Décryptagéo, l'information géographique
• Cybergeo
• Revue Internationale de Géomatique (RIG)
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• Mappemonde

Du côté des éditeurs

• Imagerie Géospatiale
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• arcOpole - Actualités du Programme
• Géoclip, le générateur d'observatoires cartographiques
• Blog GEOCONCEPT FR

Toile géomatique francophone

• Géoblogs (GeoRezo.net)
• Geotribu
• Les cafés géographiques
• UrbaLine (le blog d'Aline sur l'urba, la géomatique, et l'habitat)
• Séries temporelles (CESBIO)
• Datafoncier, données pour les territoires (Cerema)
• Cartes et figures du monde
• SIGEA: actualités des SIG pour l'enseignement agricole
• Data and GIS tips
• Neogeo Technologies
• ReLucBlog
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• My Geomatic
• archeomatic (le blog d'un archéologue à l’INRAP)
• Cartographies numériques
• Veille cartographie
• Makina Corpus
• Oslandia
• Camptocamp
• Carnet (neo)cartographique
• Le blog de Geomatys
• GEOMATIQUE
• Geomatick
• CartONG (actualités)

Géomatique anglophone

• Planet Geospatial - http://planetgs.com
• Google Maps Mania
• All Points Blog
• Directions Media - Podcasts
• Navx
• James Fee GIS Blog
• Open Geospatial Consortium (OGC)
• Planet OSGeo

Blogs GeoRezo.net

• Parcell'air
• Le coin de l'open-source géospatial
• INSPIRE by clouds
• Les GéoBlogs
• Infobulle
• Le blog SIG & URBA
• Blog Interopérabilité & GéoInformation

• 18 June 2019
• 20:00

  The Legacy of Redlining

  sur Google Maps Mania
  Under President Franklin D. Roosevelt's New Deal black homeowners were discriminated against through the creation of redlining maps. These maps identified areas with significant black populations and labeled them as too high risk for mortgage support. Black homeowners living in these areas were therefore very unlikely to be successful when trying to refinance home mortgages from the
• 18:00

  Get Your Global Heating Stripes

  sur Google Maps Mania
  Last year Ed Hawkins, a climate scientist at the National Centre for Atmospheric Science at the University of Reading, released a powerful data visualization to illustrate how temperatures have risen around the globe over the last century. His warming stripes visualization shows the average yearly temperature for every year over 100+ years. You can now get your own warming stripes for different

• 14:45

  THEIA's Sentinel-2 L2A processing on Sahel is progressing

  sur Séries temporelles (CESBIO)

  =>

  As we had announced in November, the MUSCATE production centre in Theia is gradually adding areas in the Sahel, which are shown in the image below. The data are processed from December 2016 onwards, which means that we have a large amount of data to process. So we started with the most westerly tiles, in Senegal on the UTM28 zone, then progressed from one zone to another towards the East.

  
  

  Theia Sentinel-2 processing area on the Sahel

   

  

  In red, the tiles available from Dec 2016 to NRT, in blue , the remaining tiles to be added.

  In recent days, Theia has completed the processing of tiles in the UTM29 area, which mainly covers northern Guinea and western Mali, but also partially covers southern Mauritania and Sierra Leone and north-western Côte d'Ivoire. The treatment of the UTM 30 zone, which covers Burkina Faso and Mali, is also well advanced. The east of this area is finished, and the west is progressing well, as shown in the animation below. The UTM31 zone has also been brought into production. Feel free to take a look from time to time at the map of areas covered by MUSCATE. The blue tiles turn red as soon as we switch to run-of-river processing.

   

  The data can be downloaded from here:

  [https:]]

   

  

  Animation in the region of the city of Mopti, Mali, with about one image per month in 2017. The displayed time series extends between two rainy seasons and covers the dry season. Many fire scars are visible during the dry season. Some shadows appear, which actually correspond to the shadows of cirrus clouds corrected by MAJA. Shadows and cirrus are marked in the products.

• 14:35

  Fernando Quadro: Filtrando dados em requisições WFS

  sur Planet OSGeo

  Prezados leitores,

  Provavelmente é de seu conhecimento que podemos filtrar o resultado de uma consulta WFS (registros) através do CQL Filter. Porém, caso você não tenha conhecimento existe uma outra forma de filtrar seus dados em uma requisição WFS.

  Imagine que você possui uma view no seu banco de dados, e ela está configurada como uma camada no seu GeoServer. Esta view possui 23 colunas, porém você só precisa utilizar 3 colunas. Você acha que tem necessidade de você ficar trazendo informações de 20 colunas que você não vai utilizar em cada requisição WFS?

  A resposta é não. Você pode utilizar o parâmetro PropertyName na sua requisição WFS e informar o nome das colunas que você realmente necessita. Veja como:

  https://localhost:8080/geoserver/wfs?request=GetFeature&service=WFS&version=1.0.0&typeName=topp:states&outputFormat=csv&PropertyName=(STATE_NAME,STATE_ABBR,WORKERS)
  

  Com a requisição acima, recebemos apenas as colunas que realmente iremos utilizar, e com isso diminuímos o trafego de informações “desnecessárias” transitando na rede de dados e aumentamos a performance da nossa aplicação.

• 7:15

  Changez la vie de vos commerciaux terrain : mettez de l’intelligence géographique dans leur CRM !

  sur Blog GEOCONCEPT FR
  

  Avant de rejoindre GEOCONCEPT en tant qu'Alliance Manager, Estelle Bertin a été Responsable Commerciale Mobile dans plusieurs entreprises pendant 8 ans. Retrouvez son témoignage sur les bénéfices de l'intégration de la dimension géographique dans un outil CRM.