Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
复制标题
DOI：
10.1038/s41467-020-16185-w
复制
发表时间：
2020-05-22
影响因子：
16.6
通讯作者：
Burke, Marshall
中科院分区：
文献类型：
--
作者：
Yeh, Christopher;Perez, Anthony;Burke, Marshall
经济福祉的准确和全面的衡量是研究和政策的基本投入，但是在世界许多地方，这些措施在地方一级无法获得。在这里，我们训练深度学习模型，以预测来自公开可用的多光谱卫星图像的20,000个非洲村庄的资产财富的估计。模型可以解释未经培训模型的国家的70％的地面村庄财富变化，超过了高分辨率图像的先前基准，并与普查的独立财富测量结果进行了比较，这表明卫星估计中的错误与现有地面数据中的错误相当。基于卫星的估计还可以解释多达50％的地区财富变化随着时间的变化的变化，白天图像在这项任务中特别有用。我们证明了基于卫星的研究和政策估算的实用性，并通过为非洲人口最多的国家创建财富图来证明其可扩展性。
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 similar to 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.