Editing Remote Sensing

''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. 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>

* Mapping power grids and solar panel locations.
* 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]

==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://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)]
* [https://worldview.earthdata.nasa.gov/ NASA Worldview]
* [https://www.google.com/earth/ Google Earth] (permission needed for research use)

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

* [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

* [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.

==== 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]

== References ==
@@ Line 11: / Line 11: @@
 * [https://www.earthdatascience.org/courses/ GIS & Remote sensing at Earth Lab, University of Colorado]
-*[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==
@@ Line 32: / Line 29: @@
 * [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