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> Some general-purpose remote sensing datasets are listed below. |
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TODO starting page for remote sensing |
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== Satellite imagery datasets == |
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** Public |
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==== Public datasets ==== |
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*** NASA Worldview |
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*** Google Earth |
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* [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)] |
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** Commercial |
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* [https://worldview.earthdata.nasa.gov/ NASA Worldview] |
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'''Commercial datasets''' |
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** Public |
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*** Copernicus dataset (Sentinel satellites) |
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*** BigEarthNet dataset (Sentinel satellites) |
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** Commercial |
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*** Digital Globe |
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*** Planet |
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==== Public datasets ==== |
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** See Table 1 of this survey of hyperspectral earth observation satellites for comparisons between sources. |
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** Public |
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* [https://www.copernicus.eu/en/access-data Copernicus (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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==== Public datasets ==== |
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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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== Street view datasets == |
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* [https://www.openstreetmap.org/ OpenStreetMap] |
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== References == |
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Revision as of 04:12, 28 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] Some general-purpose remote sensing datasets are listed below.
Satellite imagery datasets
High-resolution RGB satellite images (for visual predictions)
Public datasets
- United States Geological Survey
- Copernicus (Sentinel satellites)
- NASA Worldview
- Google Earth (permission needed for research use)
Commercial datasets
- DigitalGlobe (up to 31cm resolution)
- Planet (up to 72cm resolution)
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
- ↑ "Tackling climate change in the EU". Climate Change and Law Collection.
- ↑ 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.