Significance Monitoring compliance with environmental regulations is a global challenge. It is particularly difficult for governments in low-income countries, where informal industry is responsible for a large amount of pollution, because the governments lack the ability to locate and monitor large numbers of dispersed polluters. This study demonstrates an accurate, scalable machine-learning approach for identifying brick kilns, a highly polluting informal industry in Bangladesh, in satellite imagery. Our data reveal widespread violations of the national regulations governing brick manufacturing, which has implications for the health and well-being of the country. Our approach offers a low-cost, replicable method for regulatory agencies to generate information on key pollution sources.