A generalizable and accessible approach to machine learning with global satellite imagery

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of sat...

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Detalles Bibliográficos
Autores principales: Rolf, Esther, Proctor, Jonathan, Carleton, Tamma, Bolliger, Ian, Shankar, Vaishaal, Ishihara, Miyabi, Recht, Benjamin, Hsiang, Solomon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292408/
https://www.ncbi.nlm.nih.gov/pubmed/34285205
http://dx.doi.org/10.1038/s41467-021-24638-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292408/
https://www.ncbi.nlm.nih.gov/pubmed/34285205
http://dx.doi.org/10.1038/s41467-021-24638-z

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