Remote Sensing

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.

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


 * Copernicus (Sentinel satellites)
 * BigEarthNet (Sentinel satellites)

Commercial datasets


 * Digital Globe
 * Planet

Hyperspectral satellite images (up to a few hundred visible and infrared bands)
Public datasets


 * United States Geological Survey (Hyperion data)

See also


 * 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

 * OpenStreetMap