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Much environmental enforcement in the United States has historically reliedon either self-reported data or physical, resource-intensive, infrequentinspections. Advances in remote sensing and computer vision, however, have thepotential to augment compliance monitoring by detecting early warning signs ofnoncompliance. We demonstrate a process for rapid identification of significantstructural expansion using Planet's 3m/pixel satellite imagery products andfocusing on Concentrated Animal Feeding Operations (CAFOs) in the US as a testcase. Unpermitted building expansion has been a particular challenge withCAFOs, which pose significant health and environmental risks. Using newhand-labeled dataset of 145,053 images of 1,513 CAFOs, we combinestate-of-the-art building segmentation with a likelihood-based change-pointdetection model to provide a robust signal of building expansion (AUC = 0.86).A major advantage of this approach is that it can work with higher cadence(daily to weekly), but lower resolution (3m/pixel), satellite imagery thanpreviously used in similar environmental settings. It is also highlygeneralizable and thus provides a near real-time monitoring tool to prioritizeenforcement resources in other settings where unpermitted construction posesenvironmental risk, e.g. zoning, habitat modification, or wetland protection.

Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms