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Title: Using satellite imagery to understand and promote sustainable development

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.

 
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Award ID(s):
1651565
NSF-PAR ID:
10217926
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
American Association for the Advancement of Science (AAAS)
Date Published:
Journal Name:
Science
Volume:
371
Issue:
6535
ISSN:
0036-8075
Page Range / eLocation ID:
Article No. eabe8628
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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