  Much environmental enforcement in the United States has historically relied
on either self-reported data or physical, resource-intensive, infrequent
inspections. Advances in remote sensing and computer vision, however, have the
potential to augment compliance monitoring by detecting early warning signs of
noncompliance. We demonstrate a process for rapid identification of significant
structural expansion using Planet's 3m/pixel satellite imagery products and
focusing on Concentrated Animal Feeding Operations (CAFOs) in the US as a test
case. Unpermitted building expansion has been a particular challenge with
CAFOs, which pose significant health and environmental risks. Using new
hand-labeled dataset of 145,053 images of 1,513 CAFOs, we combine
state-of-the-art building segmentation with a likelihood-based change-point
detection 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 than
previously used in similar environmental settings. It is also highly
generalizable and thus provides a near real-time monitoring tool to prioritize
enforcement resources in other settings where unpermitted construction poses
environmental risk, e.g. zoning, habitat modification, or wetland protection.
