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Radiant Earth Foundation

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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[edit]

  • Earth Observation
  • Machine learning
  • Big data
  • List of datasets for machine learning research

Notes[edit]

  1. 1.0 1.1 Totaro, Paola (3 March 2017). "Daten für alle – Gates startet Satelliten-Projekt". Reuters Weltnachrichten. Retrieved 9 October 2020. Unknown parameter |url-status= ignored (help)
  2. "Radiant Earth Annual Report 2019" (PDF). 2020. Unknown parameter |url-status= ignored (help)
  3. Demyanov, Vladislav (2020). Satellites Missions and Technologies for Geosciences. IntechOpen. p. 117. ISBN 978-1-78985-995-9. Search this book on Amazon.com Logo.png
  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].
  6. 6.0 6.1 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. 9.0 9.1 "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

External links[edit]



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