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The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a deep learning algorithm to detect unreported industry sites in high-resolution satellite data. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement data sets of heavy industry sites.

Original publication




Conference paper


Cambridge University Press

Publication Date





computer vision, FFR, air pollution, remote sensing, clouds, classification, aerosols