# Mapping industrial poultry operations at scale with deep learning and aerial imagery

arXiv: https://arxiv.org/abs/2112.10988 · HTML: https://mlsanity.com/paper/2112.10988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 94 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (2019)
- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Rotation equivariant vector field networks](https://mlsanity.com/paper/1612.09346/) (2016)
- [Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities](https://mlsanity.com/paper/2005.01094/) (2020)
- [Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data](https://mlsanity.com/paper/1910.03910/) (2019)
- [Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models](https://mlsanity.com/paper/1803.06253/) (2018)
- [Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations](https://mlsanity.com/paper/1805.12219/) (2018)
- [Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery](https://mlsanity.com/paper/2103.09787/) (2021)
- [Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms](https://mlsanity.com/paper/2105.14159/) (2021)

## Built on by (most cited first, 6 of 6)

- [Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery](https://mlsanity.com/paper/2503.14860/) (2025)
- [Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act](https://mlsanity.com/paper/2208.08919/) (2022)
- [Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection](https://mlsanity.com/paper/2507.18513/) (2025)
- [AI-Driven Carbon Monitoring: Transformer-Based Reconstruction of Atmospheric CO2 in Canadian Poultry Regions](https://mlsanity.com/paper/2510.23663/) (2025)
- [PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs](https://mlsanity.com/paper/2601.11451/) (2026)
- [CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery](https://mlsanity.com/paper/2606.00548/) (2026)

## Abstract (arXiv)

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.