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Machine learning can be applied to remote sensing data to infer climate-relevant information. Some selected examples include:
- Mapping power grids and solar panel locations.
- Mapping building footprints.
- Pinpointing occurrences of deforestation.
- Creating an inventory of global greenhouse gas emissions.
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]
- United States Geological Survey
- Copernicus (Sentinel satellites)
- NASA Worldview
- Google Earth (permission needed for research use)
Multispectral satellite images (5-13 visible and infrared bands)[edit | edit source]
Hyperspectral satellite images (up to a few hundred visible and infrared bands)[edit | edit source]
- Table 1 of the review "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context" for comparisons between sources.
General satellite images[edit | edit source]
- Awesome Satellite Imagery Datasets: List of aerial and satellite imagery datasets with annotations for computer vision and deep learning.
Street view datasets[edit | edit source]
References[edit | edit source]
- "Tackling climate change in the EU". Climate Change and Law Collection.
- 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.