Difference between revisions of "Remote Sensing"

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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>
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Machine learning can be applied to remote sensing data to infer climate-relevant information. Some 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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* Mapping power grids and solar panel locations.
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* Mapping building footprints.
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* Pinpointing occurrences of deforestation.
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* [[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==

Revision as of 20:03, 31 August 2020

Machine learning can be applied to remote sensing data to infer climate-relevant information. Some examples include:[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.