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:
- Mapping power grids and solar panel locations.
- Mapping building footprints.
- Pinpointing occurrences of deforestation.
- Creating an inventory of global greenhouse gas emissions.
Online Courses and Course Materials
- GIS & Remote sensing at Earth Lab, University of Colorado
- EO College, European Space Agency
- 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
Satellite imagery datasets
High-resolution RGB satellite images (for visual predictions)
- United States Geological Survey
- Copernicus (Sentinel satellites)
- NASA Worldview
- Google Earth (permission needed for research use)
- Sentinel Hub (Sentinel, Landsat, Envisat, etc.)
Multispectral satellite images (5-13 visible and infrared bands)
Hyperspectral satellite images (up to a few hundred visible and infrared bands)
- 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
- Awesome Satellite Imagery Datasets: List of aerial and satellite imagery datasets with annotations for computer vision and deep learning.
Street view datasets
- "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.