# Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms

arXiv: https://arxiv.org/abs/2105.14159 · HTML: https://mlsanity.com/paper/2105.14159/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 73 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery](https://mlsanity.com/paper/2011.07369/) (2020)
- [A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery](https://mlsanity.com/paper/2104.01263/) (2021)
- [Large-scale detection and categorization of oil spills from SAR images with deep learning](https://mlsanity.com/paper/2006.13575/) (2020)
- [Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery](https://mlsanity.com/paper/2103.09787/) (2021)
- [A clustering approach to heterogeneous change detection](https://mlsanity.com/paper/1702.03176/) (2017)

## Built on by (most cited first, 4 of 4)

- [Mapping industrial poultry operations at scale with deep learning and aerial imagery](https://mlsanity.com/paper/2112.10988/) (2021)
- [Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act](https://mlsanity.com/paper/2208.08919/) (2022)
- [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)

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.