Remote Sensing: Difference between revisions

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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>
''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==
 
==Background Readings==
 
==Online Courses and Course Materials==
 
==Online Courses and Course Materials==
 
* [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==
 
==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==
 
==Libraries and Tools==
 
== Data ==
 
== Data ==
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=== Satellite imagery datasets ===
 
=== Satellite imagery datasets ===
   
====High-resolution RGB satellite images (for visual predictions)====
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===='''High-resolution RGB satellite images (for visual predictions)'''====
 
Public datasets
 
Public datasets
   
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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
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*[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) ====
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===='''Multispectral satellite images (5-13 visible and infrared bands)''' ====
 
Public datasets
 
Public datasets
   
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* [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)'''====
 
Public datasets
 
Public datasets
   
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* 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.
 
==== 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 ===
 
=== Street view datasets ===

Revision as of 19:55, 31 August 2020

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.[1]

Background Readings

Online Courses and Course Materials

Community

Libraries and Tools

Data

Satellite imagery datasets

High-resolution RGB satellite images (for visual predictions)

Public datasets

Commercial datasets

Multispectral satellite images (5-13 visible and infrared bands)

Public datasets

Commercial datasets

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

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.

Street view datasets

References

  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.