卫星图像
卫星
深度学习
多光谱图像
计算机科学
变化(天文学)
资产(计算机安全)
任务(项目管理)
可扩展性
遥感
计量经济学
数据科学
环境资源管理
地理
人工智能
环境科学
经济
工程类
管理
航空航天工程
物理
数据库
天体物理学
计算机安全
作者
Christopher Yeh,Anthony Perez,Anne Driscoll,George Azzari,Zhongyi Tang,David B. Lobell,Stefano Ermon,Marshall Burke
标识
DOI:10.1038/s41467-020-16185-w
摘要
Abstract Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution imagery, and comparison with independent wealth measurements from censuses suggests that errors in satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime imagery particularly useful in this task. We demonstrate the utility of satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa’s most populous country.
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