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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 [https://en.wikipedia.org/wiki/Remote_sensing Wikipedia page] on this topic.''
Machine learning can be applied to remote sensing data to infer climate-relevant information such as global greenhouse gas emissions, building footprints, solar panel locations, or occurrences of deforestation.<ref>{{Cite web|title=Tackling climate change in the EU|url=http://dx.doi.org/10.1163/9789004322714_cclc_2017-0189-005|website=Climate Change and Law Collection}}</ref> Some general-purpose remote sensing datasets are listed below.
 
Machine learning can be applied to remote sensing data to infer climate-relevant information. suchSome asselected globalexamples greenhouse gas emissions, building footprints, solar panel locations, or occurrences of deforestation.include:<ref>{{Cite web|title=Tackling climate change in the EU|url=http://dx.doi.org/10.1163/9789004322714_cclc_2017-0189-005|website=Climate Change and Law Collection}}</ref> Some general-purpose remote sensing datasets are listed below.
== Satellite imagery datasets ==
 
* Mapping power grids and solar panel locations.
=== '''High-resolution RGB satellite images (for visual predictions)''' ===
* Mapping building footprints.
* Pinpointing occurrences of deforestation.
* [[Greenhouse Gas Emissions Detection|Creating an inventory of global greenhouse gas emissions]].
==Background Readings==
==Online Courses and Course Materials==
 
* [https://www.earthdatascience.org/courses/ GIS & Remote sensing at Earth Lab, University of Colorado]
==== Public datasets ====
*[https://eo-college.org/welcome/ EO College, European Space Agency]
*Coursera:
**[https://www.coursera.org/learn/remote-sensing/home/welcome Remote Sensing Image Acquisition, Analysis and Applications]
 
==Community==
 
*'''EARTHVISION''': A workshop regularly held at computer vision conferences. Website [https://www.grss-ieee.org/earthvision2020/ here].
*'''Space and AI:''' A conference organized by the ESA-CLAIRE AI Special Interest Group on Space. Website [https://claire-ai.org/sig-space/?lang=fr here].
 
==Libraries and Tools==
== Data ==
 
=== Satellite imagery datasets ===
 
=== '''=High-resolution RGB satellite images (for visual predictions)''' ====
==== Public datasets ====
 
* [https://earthexplorer.usgs.gov/ United States Geological Survey]
* [https://worldview.earthdata.nasa.gov/ NASA Worldview]
* [https://www.google.com/earth/ Google Earth] (permission needed for research use)
*[https://apps.sentinel-hub.com/eo-browser/ Sentinel Hub] (Sentinel, Landsat, Envisat, etc.)
 
'''Commercial datasets'''
 
*[https://www.digitalglobe.com/ DigitalGlobe] (up to 31cm resolution)
*[https://www.planet.com/ Planet] (up to 72cm resolution)
 
=== '''=Multispectral satellite images (5-13 visible and infrared bands)''' ====
==== Public datasets ====
 
==== Public datasets ====
 
* [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)]
* [http://bigearth.net/ BigEarthNet (Sentinel satellites)]
 
==== Commercial datasets ====
 
* [https://www.digitalglobe.com/ Digital Globe]
* [https://www.planet.com/ Planet]
 
=== '''=Hyperspectral satellite images (up to a few hundred visible and infrared bands)''': ====
==== Public datasets ====
 
==== Public datasets ====
 
* [https://earthexplorer.usgs.gov/ 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"<ref>{{Cite journal|last=Transon|first=Julie|last2=d’Andrimont|first2=Raphaël|last3=Maugnard|first3=Alexandre|last4=Defourny|first4=Pierre|date=2018-01-23|title=Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context|url=http://dx.doi.org/10.3390/rs10020157|journal=Remote Sensing|volume=10|issue=3|pages=157|doi=10.3390/rs10020157|issn=2072-4292}}</ref> for comparisons between sources.
 
==== StreetGeneral viewsatellite datasetsimages ====
 
* [https://github.com/chrieke/awesome-satellite-imagery-datasets Awesome Satellite Imagery Datasets]: List of aerial and satellite imagery datasets with annotations for computer vision and deep learning.
 
=== Street view datasets ===
 
* [https://www.openstreetmap.org/ OpenStreetMap]
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