Mapping Industrial Poultry Operations at Scale With Deep Learning and Aerial Imagery

Caleb Robinson, Ben Chugg, Brandon R. Anderson, Juan M. Lavista Ferres, Daniel E. Ho

Abstract: Concentrated animal feeding operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountability Office notes that a basic challenge is the lack of comprehensive location information on CAFOs. We use the U.S. Department of Agriculture's National Agricultural Imagery Program 1 m/pixel aerial imagery to detect poultry CAFOs across the continental USA. We train convolutional neural network models to identify individual poultry barns and apply the best-performing model to over 42 TB of imagery to create the first national open-source dataset of poultry CAFOs We validate the model predictions against held-out validation set on poultry CAFO facility locations from ten hand-labeled counties in California and demonstrate that this approach has significant potential to fill gaps in environmental monitoring.