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

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

This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.

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Autores principales: Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Q
Acceso en línea:https://doaj.org/article/35ce16c6215c446488bcca5b7caf036c
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spelling oai:doaj.org-article:35ce16c6215c446488bcca5b7caf036c2021-12-02T17:56:56ZA generalizable and accessible approach to machine learning with global satellite imagery10.1038/s41467-021-24638-z2041-1723https://doaj.org/article/35ce16c6215c446488bcca5b7caf036c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24638-zhttps://doaj.org/toc/2041-1723This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.Esther RolfJonathan ProctorTamma CarletonIan BolligerVaishaal ShankarMiyabi IshiharaBenjamin RechtSolomon HsiangNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Esther Rolf
Jonathan Proctor
Tamma Carleton
Ian Bolliger
Vaishaal Shankar
Miyabi Ishihara
Benjamin Recht
Solomon Hsiang
A generalizable and accessible approach to machine learning with global satellite imagery
description This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.
format article
author Esther Rolf
Jonathan Proctor
Tamma Carleton
Ian Bolliger
Vaishaal Shankar
Miyabi Ishihara
Benjamin Recht
Solomon Hsiang
author_facet Esther Rolf
Jonathan Proctor
Tamma Carleton
Ian Bolliger
Vaishaal Shankar
Miyabi Ishihara
Benjamin Recht
Solomon Hsiang
author_sort Esther Rolf
title A generalizable and accessible approach to machine learning with global satellite imagery
title_short A generalizable and accessible approach to machine learning with global satellite imagery
title_full A generalizable and accessible approach to machine learning with global satellite imagery
title_fullStr A generalizable and accessible approach to machine learning with global satellite imagery
title_full_unstemmed A generalizable and accessible approach to machine learning with global satellite imagery
title_sort generalizable and accessible approach to machine learning with global satellite imagery
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/35ce16c6215c446488bcca5b7caf036c
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