November 2024
Intermediate to advanced
282 pages
7h 44m
French
Content preview from Python pour l'analyse des données géospatiales
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Chapitre 4. L'analyse géospatiale dans le Cloud : Google Earth Engine et autres outils
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Comment accèdes-tu aux données géospatiales ? Bien que les professionnels des données disposant d'un compte d'entreprise ne pensent pas forcément aux limites de l'informatique personnelle et au fait de s'appuyer sur des données open source, le reste d'entre nous travaille souvent dans des limites. L'analyse géospatiale dans le Cloud a réduit le fossé, puisque cela signifie que nous n'avons plus besoin de stocker de gros volumes de données localement. Jamais auparavant le grand public n'avait eu accès en open source à des données géospatiales à une telle échelle mondiale. Ce chapitre te montrera où trouver des données pour l'exploration et l'apprentissage.
Les programmes spatiaux aux États-Unis et dans le monde entier recueillent des données à partir de satellites et de capteurs depuis des décennies, mais ce n'est que récemment que nous avons eu la capacité de manipuler ces données en temps réel à des fins d'analyse. L'USGS héberge EarthExplorer (Landsat), et le Copernicus Open Access Hub fournit des données provenant des satellites Sentinel de l'Agence spatiale européenne (ESA). Les images satellites Landsat à haute résolution nous permettent d'évaluer et de mesurer les changements environnementaux, de comprendre l'impact de la science du climat et des pratiques ...
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