Remote Sensing
Machine learning can be applied to remote sensing data to infer climate-relevant information. Some examples include:[1]
- Mapping power grids and solar panel locations.
- Mapping building footprints.
- Pinpointing occurrences of deforestation.
- Creating an inventory of global greenhouse gas emissions.
Background Readings
Online Courses and Course Materials
Community
Libraries and Tools
Data
Satellite imagery datasets
High-resolution RGB satellite images (for visual predictions)
Public datasets
- United States Geological Survey
- Copernicus (Sentinel satellites)
- NASA Worldview
- Google Earth (permission needed for research use)
Commercial datasets
- DigitalGlobe (up to 31cm resolution)
- Planet (up to 72cm resolution)
Multispectral satellite images (5-13 visible and infrared bands)
Public datasets
Commercial datasets
Hyperspectral satellite images (up to a few hundred visible and infrared bands)
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
- Awesome Satellite Imagery Datasets: List of aerial and satellite imagery datasets with annotations for computer vision and deep learning.
Street view datasets
References
- ↑ "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.