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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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| Veröffentlicht in: | Nat Commun |
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| Hauptverfasser: | , , , , , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Nature Publishing Group UK
2021
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| Schlagworte: | |
| Online Zugang: | 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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