Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and satellite imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measur...

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Autores principales:	Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, Marshall Burke
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7795998e04994473a909cfaf24b6daf9
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spelling	oai:doaj.org-article:7795998e04994473a909cfaf24b6daf92021-12-02T15:52:23ZUsing publicly available satellite imagery and deep learning to understand economic well-being in Africa10.1038/s41467-020-16185-w2041-1723https://doaj.org/article/7795998e04994473a909cfaf24b6daf92020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16185-whttps://doaj.org/toc/2041-1723It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and satellite imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution imagery with errors comparable to that of existing ground data.Christopher YehAnthony PerezAnne DriscollGeorge AzzariZhongyi TangDavid LobellStefano ErmonMarshall BurkeNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Christopher Yeh Anthony Perez Anne Driscoll George Azzari Zhongyi Tang David Lobell Stefano Ermon Marshall Burke Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
description	It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and satellite imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution imagery with errors comparable to that of existing ground data.
format	article
author	Christopher Yeh Anthony Perez Anne Driscoll George Azzari Zhongyi Tang David Lobell Stefano Ermon Marshall Burke
author_facet	Christopher Yeh Anthony Perez Anne Driscoll George Azzari Zhongyi Tang David Lobell Stefano Ermon Marshall Burke
author_sort	Christopher Yeh
title	Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
title_short	Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
title_full	Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
title_fullStr	Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
title_full_unstemmed	Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
title_sort	using publicly available satellite imagery and deep learning to understand economic well-being in africa
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7795998e04994473a909cfaf24b6daf9
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Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

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