  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 USDA's National
Agricultural Imagery Program (NAIP) 1m/pixel aerial imagery to detect poultry
CAFOs across the continental United States. We train convolutional neural
network (CNN) 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 10 hand-labeled
counties in California and demonstrate that this approach has significant
potential to fill gaps in environmental monitoring.
