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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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| Publicado no: | Nat Commun |
|---|---|
| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8292408/ https://ncbi.nlm.nih.gov/pubmed/34285205 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-021-24638-z |
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