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corrected expert tag to specify wikiproject computer science (as this seems the closest to artificial intelligence)
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Added two more sources. One book chapter from 2020 from Springer Science book on "Space Fostering African Societies", another covering the work of radiant earth extensively from a scientific and independent point of view (Zenke da Cruc Radiant Earth Platform ...)
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'''Radiant Earth Foundation''' is an American non-profit organization founded in 2016.<ref name=":0">{{Cite news|last=Totaro|first=Paola|date=3 March 2017|title=Daten für alle – Gates startet Satelliten-Projekt|work=Reuters Weltnachrichten|url=https://www.reuters.com/article/usa-satelliten-bill-gates-idDEKBN16A13U|url-status=live|access-date=9 October 2020}}</ref><ref>{{Cite news|last=|first=|date=2020|title=Radiant Earth Annual Report 2019|work=|url=https://s3-us-west-2.amazonaws.com/radiant-blog-assets/wp-content/uploads/2020/04/15230328/2019-Annual-Report.pdf|url-status=live|access-date=}}</ref> Its goal is to apply machine learning for Earth observation<ref>{{Cite book|last=Demyanov|first=Vladislav|title=Satellites Missions and Technologies for Geosciences|publisher=IntechOpen|year=2020|isbn=978-1-78985-995-9|location=|pages=117}}</ref> to meet the [[Sustainable Development Goals]].<ref>{{Cite web|title=Radiant Earth Foundation|url=http://www.data4sdgs.org/partner/radiant-earth-foundation|access-date=2020-08-27|website=www.data4sdgs.org}}</ref> The foundation works on developing openly licensed [[Earth observation]] [[machine learning]] libraries, training data sets<ref>{{cite arxiv|last=Nachmany|first=Yoni|date=14 November 2018|title=Generating a Training Dataset for Land Cover Classification to Advance Global Development|volume=|pages=|class=cs.CV|eprint=1811.07998}}</ref> and models through an [[open source]] hub that support missions worldwide<ref>{{Cite web|title=Radiant Earth Foundation Releases First Earth Imagery Platform for Global Development – Tanzania News Gazette|url=https://www.tanzanianewsreports.com/radiant-earth-foundation-releases-first-earth-imagery-platform-for-global-development/|access-date=2020-10-09|language=en-US}}</ref> like agriculture,<ref>{{Cite journal|last=Ballantynwe|first=A.|date=2019|title=Benchmark Agricultural Training Datasets to Create Regional Crop Type Classification Models from Earth Observations|url=https://ui.adsabs.harvard.edu/abs/2019AGUFMGC23H1439B/abstract|journal=American Geophysical Union, Fall Meeting 2019, Abstract #GC23H-1439|volume=2019|pages=GC23H–1439|bibcode=2019AGUFMGC23H1439B|via=}}</ref> conservation, and climate change.<ref name="About – Radiant Earth Foundation">{{Cite web|title=About – Radiant Earth Foundation|url=https://www.radiant.earth/about/|access-date=2020-08-27|language=en-US}}</ref> Radiant Earth also works on a [[community of practice]] that develop standards around machine learning for Earth observation.
'''Radiant Earth Foundation''' is an American non-profit organization founded in 2016.<ref name=":0">{{Cite news|last=Totaro|first=Paola|date=3 March 2017|title=Daten für alle – Gates startet Satelliten-Projekt|work=Reuters Weltnachrichten|url=https://www.reuters.com/article/usa-satelliten-bill-gates-idDEKBN16A13U|url-status=live|access-date=9 October 2020}}</ref><ref>{{Cite news|last=|first=|date=2020|title=Radiant Earth Annual Report 2019|work=|url=https://s3-us-west-2.amazonaws.com/radiant-blog-assets/wp-content/uploads/2020/04/15230328/2019-Annual-Report.pdf|url-status=live|access-date=}}</ref> Its goal is to apply machine learning for Earth observation<ref>{{Cite book|last=Demyanov|first=Vladislav|title=Satellites Missions and Technologies for Geosciences|publisher=IntechOpen|year=2020|isbn=978-1-78985-995-9|location=|pages=117}}</ref> to meet the [[Sustainable Development Goals]].<ref>{{Cite web|title=Radiant Earth Foundation|url=http://www.data4sdgs.org/partner/radiant-earth-foundation|access-date=2020-08-27|website=www.data4sdgs.org}}</ref> The foundation works on developing openly licensed [[Earth observation]] [[machine learning]] libraries, training data sets<ref>{{cite arxiv|last=Nachmany|first=Yoni|date=14 November 2018|title=Generating a Training Dataset for Land Cover Classification to Advance Global Development|volume=|pages=|class=cs.CV|eprint=1811.07998}}</ref> and models through an [[open source]] hub<ref name=":1">{{Cite journal|last=Zenke da Cruz|first=Camila Lauria|date=2019|title=Radiant Earth Platform: POTENCIALIDADES E LIMITAÇÕES DE ABORDAGEM DE PROCESSAMENTO DIGITAL DE IMAGEM NA NUVEM|url=http://marte2.sid.inpe.br/col/sid.inpe.br/marte2/2019/10.08.13.18/doc/97805.pdf|journal=Anais do XIX Simpósio Brasileiro de Sensoriamento Remoto|volume=|pages=|isbn=978-85-17-00097-3|via=}}</ref> that support missions worldwide<ref>{{Cite web|title=Radiant Earth Foundation Releases First Earth Imagery Platform for Global Development – Tanzania News Gazette|url=https://www.tanzanianewsreports.com/radiant-earth-foundation-releases-first-earth-imagery-platform-for-global-development/|access-date=2020-10-09|language=en-US}}</ref> like agriculture,<ref>{{Cite journal|last=Ballantynwe|first=A.|date=2019|title=Benchmark Agricultural Training Datasets to Create Regional Crop Type Classification Models from Earth Observations|url=https://ui.adsabs.harvard.edu/abs/2019AGUFMGC23H1439B/abstract|journal=American Geophysical Union, Fall Meeting 2019, Abstract #GC23H-1439|volume=2019|pages=GC23H–1439|bibcode=2019AGUFMGC23H1439B|via=}}</ref> conservation, and climate change.<ref name="About – Radiant Earth Foundation">{{Cite web|title=About – Radiant Earth Foundation|url=https://www.radiant.earth/about/|access-date=2020-08-27|language=en-US}}</ref> Radiant Earth also works on a [[community of practice]] that develop standards, templates and APIs<ref name=":1" /> around machine learning for Earth observation. According to scholar David Lindgren, the foundation „serves to make satellite imagery widely accessible and usable for development practitioners“<ref>{{Citation|last=Lindgren|first=David|title=Satellites and Their Potential Role in Supporting the African Union’s Continental Early Warning System|date=2020|url=https://doi.org/10.1007/978-3-030-32930-3_13|work=Space Fostering African Societies: Developing the African Continent through Space, Part 1|pages=195–205|editor-last=Froehlich|editor-first=Annette|series=Southern Space Studies|place=Cham|publisher=Springer International Publishing|language=en|doi=10.1007/978-3-030-32930-3_13|isbn=978-3-030-32930-3|access-date=2020-10-26}}</ref>.


The Foundation is funded by Schmidt Futures, [[Bill & Melinda Gates Foundation]],<ref name=":0" /> McGovern Foundation and the [[Omidyar Network|Omidyar network]]<ref name="About – Radiant Earth Foundation"/>
The Foundation is funded by Schmidt Futures, [[Bill & Melinda Gates Foundation]],<ref name=":0" /> McGovern Foundation and the [[Omidyar Network|Omidyar network]]<ref name="About – Radiant Earth Foundation"/>

Revision as of 20:12, 26 October 2020

Radiant Earth Foundation is an American non-profit organization founded in 2016.[1][2] Its goal is to apply machine learning for Earth observation[3] to meet the Sustainable Development Goals.[4] The foundation works on developing openly licensed Earth observation machine learning libraries, training data sets[5] and models through an open source hub[6] that support missions worldwide[7] like agriculture,[8] conservation, and climate change.[9] Radiant Earth also works on a community of practice that develop standards, templates and APIs[6] around machine learning for Earth observation. According to scholar David Lindgren, the foundation „serves to make satellite imagery widely accessible and usable for development practitioners“[10].

The Foundation is funded by Schmidt Futures, Bill & Melinda Gates Foundation,[1] McGovern Foundation and the Omidyar network[9]

See also

Notes

  1. ^ a b Totaro, Paola (3 March 2017). "Daten für alle – Gates startet Satelliten-Projekt". Reuters Weltnachrichten. Retrieved 9 October 2020.{{cite news}}: CS1 maint: url-status (link)
  2. ^ "Radiant Earth Annual Report 2019" (PDF). 2020.{{cite news}}: CS1 maint: url-status (link)
  3. ^ Demyanov, Vladislav (2020). Satellites Missions and Technologies for Geosciences. IntechOpen. p. 117. ISBN 978-1-78985-995-9.
  4. ^ "Radiant Earth Foundation". www.data4sdgs.org. Retrieved 2020-08-27.
  5. ^ Nachmany, Yoni (14 November 2018). "Generating a Training Dataset for Land Cover Classification to Advance Global Development". arXiv:1811.07998 [cs.CV]. {{cite arXiv}}: Cite has empty unknown parameter: |volume= (help)
  6. ^ a b Zenke da Cruz, Camila Lauria (2019). "Radiant Earth Platform: POTENCIALIDADES E LIMITAÇÕES DE ABORDAGEM DE PROCESSAMENTO DIGITAL DE IMAGEM NA NUVEM" (PDF). Anais do XIX Simpósio Brasileiro de Sensoriamento Remoto. ISBN 978-85-17-00097-3.
  7. ^ "Radiant Earth Foundation Releases First Earth Imagery Platform for Global Development – Tanzania News Gazette". Retrieved 2020-10-09.
  8. ^ Ballantynwe, A. (2019). "Benchmark Agricultural Training Datasets to Create Regional Crop Type Classification Models from Earth Observations". American Geophysical Union, Fall Meeting 2019, Abstract #GC23H-1439. 2019: GC23H–1439. Bibcode:2019AGUFMGC23H1439B.
  9. ^ a b "About – Radiant Earth Foundation". Retrieved 2020-08-27.
  10. ^ Lindgren, David (2020), Froehlich, Annette (ed.), "Satellites and Their Potential Role in Supporting the African Union's Continental Early Warning System", Space Fostering African Societies: Developing the African Continent through Space, Part 1, Southern Space Studies, Cham: Springer International Publishing, pp. 195–205, doi:10.1007/978-3-030-32930-3_13, ISBN 978-3-030-32930-3, retrieved 2020-10-26

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