Remote Sensing

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This page is about the applications of machine learning (ML) in the context of remote sensing. For an overview of remote sensing more generally, please see the Wikipedia page on this topic.

Machine learning can be applied to remote sensing data to infer climate-relevant information. Some selected examples include:[1]

Background Readings[edit | edit source]

Online Courses and Course Materials[edit | edit source]

Community[edit | edit source]

  • EARTHVISION: A workshop regularly held at computer vision conferences. Website here.
  • Space and AI: A conference organized by the ESA-CLAIRE AI Special Interest Group on Space. Website here.

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Satellite imagery datasets[edit | edit source]

High-resolution RGB satellite images (for visual predictions)[edit | edit source]

Public datasets

Commercial datasets

Multispectral satellite images (5-13 visible and infrared bands)[edit | edit source]

Public datasets

Commercial datasets

Hyperspectral satellite images (up to a few hundred visible and infrared bands)[edit | edit source]

Public datasets

See also

  • Table 1 of the review "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context"[2] for comparisons between sources.

General satellite images[edit | edit source]

Street view datasets[edit | edit source]

References[edit | edit source]

  1. "Tackling climate change in the EU". Climate Change and Law Collection.
  2. Transon, Julie; d’Andrimont, Raphaël; Maugnard, Alexandre; Defourny, Pierre (2018-01-23). "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context". Remote Sensing. 10 (3): 157. doi:10.3390/rs10020157. ISSN 2072-4292.