论文标题
卫星图像上的深层转移学习改善了发展中国家的空气质量估计
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
论文作者
论文摘要
城市空气污染是低收入和中等收入国家（LMIC）的公共卫生挑战。但是，LMIC缺乏足够的空气质量（AQ）监测基础设施。持续的挑战是我们无法在LMIC城市准确估算AQ，这阻碍了应急准备和降低风险。将卫星图像映射到AQ的基于深度学习的模型可以为具有足够地面数据的高收入国家（HIC）构建。在这里，我们证明了一种适应卫星图像上深层传输学习的可扩展方法可以根据在HIC城市中学到的时空模式中提取有意义的LMIC城市中的有意义的估计和见解。在非洲加纳的阿克拉（Accra）展示了这种方法，并从两个美国城市（尤其是洛杉矶和纽约）中学到了AQ模式。
Urban air pollution is a public health challenge in low- and middle-income countries (LMICs). However, LMICs lack adequate air quality (AQ) monitoring infrastructure. A persistent challenge has been our inability to estimate AQ accurately in LMIC cities, which hinders emergency preparedness and risk mitigation. Deep learning-based models that map satellite imagery to AQ can be built for high-income countries (HICs) with adequate ground data. Here we demonstrate that a scalable approach that adapts deep transfer learning on satellite imagery for AQ can extract meaningful estimates and insights in LMIC cities based on spatiotemporal patterns learned in HIC cities. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned from two US cities, specifically Los Angeles and New York.
