Remote Sensing: Difference between revisions

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Commercial datasets
 
Commercial datasets
   
* [https://www.digitalglobe.com/ Digital Globe]
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* [https://www.maxar.com/ Maxar (former Digital Globe; owns Earth Observation satellites)]
* [https://www.planet.com/ Planet]
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* [https://www.planet.com/ Planet (owns Earth Observation satellites)]
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* [https://www.euspaceimaging.com/ European Space Imaging (reseller; image provider)]
   
 
====Hyperspectral satellite images (up to a few hundred visible and infrared bands)====
 
====Hyperspectral satellite images (up to a few hundred visible and infrared bands)====

Revision as of 09:44, 7 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

Online Courses and Course Materials

Community

  • 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

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.

General satellite images

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.