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

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.

Keywords

• Concentrated animal feeding operations (CAF- Os)
• convolutional neural networks (CNNs)
• deep learning
• National Agricultural Imagery Program (NAIP)
• poultry barns
• semantic segmentation