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.

Guardado en:
Detalles Bibliográficos
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
Materias:
Q
Acceso en línea:https://doaj.org/article/35ce16c6215c446488bcca5b7caf036c
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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.