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Radiant Earth Foundation is an American non-profit organization founded in 2016. Its goal is to apply machine learning for Earth observation to meet the Sustainable Development Goals. The foundation works on developing openly licensed Earth observation machine learning libraries, training data sets and models through an open source hub that support missions worldwide like agriculture, conservation, and climate change. Radiant Earth also works on a community of practice that develop standards, templates and APIs 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".
The Foundation is funded by Schmidt Futures, Bill & Melinda Gates Foundation, McGovern Foundation and the Omidyar network
See also
- Earth Observation – Information about the Earth environment, remote or in situPages displaying short descriptions of redirect targets
- Machine learning – Study of algorithms that improve automatically through experience
- Big data – Extremely large or complex datasets
- List of datasets for machine learning research
Notes
- ^ Totaro, Paola (3 March 2017). "Daten für alle – Gates startet Satelliten-Projekt". Reuters Weltnachrichten. Retrieved 9 October 2020.
- "Radiant Earth Annual Report 2019" (PDF). 2020.
- Demyanov, Vladislav (2020). Satellites Missions and Technologies for Geosciences. IntechOpen. p. 117. ISBN 978-1-78985-995-9.
- "Radiant Earth Foundation". www.data4sdgs.org. Retrieved 2020-08-27.
- Nachmany, Yoni (14 November 2018). "Generating a Training Dataset for Land Cover Classification to Advance Global Development". arXiv:1811.07998 .
- ^ 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.
- "Radiant Earth Foundation Releases First Earth Imagery Platform for Global Development – Tanzania News Gazette". Retrieved 2020-10-09.
- 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.
- ^ "About – Radiant Earth Foundation". Retrieved 2020-08-27.
- 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, S2CID 213700549, retrieved 2020-10-26
External links
- "Radiant Earth Foundation – Earth Imagery for Impact". radiant.earth. Retrieved 2020-08-27.
- "Satellites and AI Can Help Solve Big Problems—If Given the Chance" Retrieved 2022-07-11
- "The billionaire philanthropists intent on using satellites to save the world" Retrieved 2022-07-11
- "Use of artificial intelligence and machine learning in NASA" Retrieved 2022-07-11
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