Remote Sensing: Difference between revisions

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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.''
TODO starting page for remote sensing
 
   
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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>
* '''High-resolution RGB satellite images (for visual predictions)''':
 
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** Public
 
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* Mapping power grids and solar panel locations.
*** United States Geological Survey
 
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* Mapping building footprints.
*** Copernicus dataset (Sentinel satellites)
 
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* Pinpointing occurrences of deforestation.
*** NASA Worldview
 
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* [[Greenhouse Gas Emissions Detection|Creating an inventory of global greenhouse gas emissions]].
** Public, but permission needed for research use
 
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==Background Readings==
*** Google Earth
 
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==Online Courses and Course Materials==
** Commercial
 
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*** DigitalGlobe (up to 31cm resolution)
 
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* [https://www.earthdatascience.org/courses/ GIS & Remote sensing at Earth Lab, University of Colorado]
*** Planet (up to 72cm resolution)
 
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*[https://eo-college.org/welcome/ EO College, European Space Agency]
* '''Multispectral satellite images (5-13 visible and infrared bands)''':
 
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*Coursera:
** Public
 
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**[https://www.coursera.org/learn/remote-sensing/home/welcome Remote Sensing Image Acquisition, Analysis and Applications]
*** Copernicus dataset (Sentinel satellites)
 
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*** BigEarthNet dataset (Sentinel satellites)
 
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==Community==
** Commercial
 
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*** Digital Globe
 
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*'''EARTHVISION''': A workshop regularly held at computer vision conferences. Website [https://www.grss-ieee.org/earthvision2020/ here].
*** Planet
 
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*'''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].
* '''Hyperspectral satellite images (up to a few hundred visible and infrared bands)''':
 
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** See Table 1 of this survey of hyperspectral earth observation satellites for comparisons between sources.
 
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==Libraries and Tools==
** Public
 
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== Data ==
*** United States Geological Survey (Hyperion 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
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* [https://earthexplorer.usgs.gov/ United States Geological Survey]
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* [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)]
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* [https://worldview.earthdata.nasa.gov/ NASA Worldview]
 
* [https://www.google.com/earth/ Google Earth] (permission needed for research use)
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*[https://apps.sentinel-hub.com/eo-browser/ Sentinel Hub] (Sentinel, Landsat, Envisat, etc.)
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Commercial datasets
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*[https://www.digitalglobe.com/ DigitalGlobe] (up to 31cm resolution)
 
*[https://www.planet.com/ Planet] (up to 72cm resolution)
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====Multispectral satellite images (5-13 visible and infrared bands) ====
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Public datasets
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* [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)]
 
* [http://bigearth.net/ BigEarthNet (Sentinel satellites)]
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Commercial datasets
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* [https://www.digitalglobe.com/ Digital Globe]
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* [https://www.planet.com/ Planet]
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====Hyperspectral satellite images (up to a few hundred visible and infrared bands)====
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Public datasets
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* [https://earthexplorer.usgs.gov/ United States Geological Survey (Hyperion data)]
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See also
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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.
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==== General satellite images ====
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* [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.
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=== Street view datasets ===
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* [https://www.openstreetmap.org/ OpenStreetMap]
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== References ==

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.