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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.''
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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.


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Machine learning can be applied to remote sensing data to infer climate-relevant information. Some selected examples 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>
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== Satellite imagery datasets ==


* Mapping power grids and solar panel locations.
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=== '''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]
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==== 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 ==

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=== Satellite imagery datasets ===

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====High-resolution RGB satellite images (for visual predictions)====
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Public datasets


* [https://earthexplorer.usgs.gov/ United States Geological Survey]
* [https://earthexplorer.usgs.gov/ United States Geological Survey]
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* [https://worldview.earthdata.nasa.gov/ NASA Worldview]
* [https://worldview.earthdata.nasa.gov/ NASA Worldview]
* [https://www.google.com/earth/ Google Earth] (permission needed for research use)
* [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'''
Commercial datasets


*[https://www.digitalglobe.com/ DigitalGlobe] (up to 31cm resolution)
*[https://www.digitalglobe.com/ DigitalGlobe] (up to 31cm resolution)
*[https://www.planet.com/ Planet] (up to 72cm resolution)
*[https://www.planet.com/ Planet] (up to 72cm resolution)


=== '''Multispectral satellite images (5-13 visible and infrared bands)''' ===
====Multispectral satellite images (5-13 visible and infrared bands) ====
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Public datasets

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==== Public datasets ====


* [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)]
* [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)]
* [http://bigearth.net/ BigEarthNet (Sentinel satellites)]
* [http://bigearth.net/ BigEarthNet (Sentinel satellites)]


==== Commercial datasets ====
Commercial datasets


* [https://www.digitalglobe.com/ Digital Globe]
* [https://www.digitalglobe.com/ Digital Globe]
* [https://www.planet.com/ Planet]
* [https://www.planet.com/ Planet]


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

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==== Public datasets ====


* [https://earthexplorer.usgs.gov/ United States Geological Survey (Hyperion data)]
* [https://earthexplorer.usgs.gov/ United States Geological Survey (Hyperion data)]


==== See also ====
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.
* 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.


== Street view datasets ==
==== General satellite images ====

* [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]
* [https://www.openstreetmap.org/ OpenStreetMap]

Latest revision as of 07:38, 9 January 2022

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:[1]

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]

Public datasets

Commercial datasets

Multispectral satellite images (5-13 visible and infrared bands)[edit | edit source]

Public datasets

Commercial datasets

Hyperspectral satellite images (up to a few hundred visible and infrared bands)[edit | edit source]

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[edit | edit source]

Street view datasets[edit | edit source]

References[edit | edit source]

  1. ↑ "Tackling climate change in the EU". Climate Change and Law Collection.
  2. ↑ 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.
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