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.