PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs

TL;DR

PRISM-CAFO introduces an explainable, infrastructure-focused pipeline for accurately detecting and characterizing CAFOs from aerial imagery, enhancing scalable monitoring and risk assessment of livestock operations.

Contribution

It presents a novel, domain-tuned approach combining object detection, structured feature extraction, and explainability for CAFO mapping from satellite and aerial images.

Findings

Achieves state-of-the-art performance, surpassing baselines by up to 15%.

Effectively links infrastructure features to classification decisions.

Supports transparent, scalable monitoring of livestock infrastructure.

Abstract

Large-scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases and extreme weather events. As the number of such operations continues to grow, accurate and scalable mapping has become increasingly important. In this work, we present an infrastructure-first, explainable pipeline for identifying and characterizing Concentrated Animal Feeding Operations (CAFOs) from aerial and satellite imagery. Our method (i) detects candidate infrastructure (e.g., barns, feedlots, manure lagoons, silos) with a domain-tuned YOLOv8 detector, then derives SAM2 masks from these boxes and filters component-specific criteria; (ii) extracts structured descriptors (e.g., counts, areas, orientations, and spatial relations) and fuses them with deep visual features using a lightweight spatial cross-attention classifier; and (iii) outputs both CAFO type predictions and mask-level attributions that link decisions to visible infrastructure. Through comprehensive evaluation, we show that our approach achieves state-of-the-art performance, with Swin-B+PRISM-CAFO surpassing the best performing baseline by up to 15\%. Beyond strong predictive performance across diverse U.S. regions, we run systematic gradient--activation analyses that quantify the impact of domain priors and show how specific infrastructure (e.g., barns, lagoons) shapes classification decisions. We release code, infrastructure masks, and descriptors to support transparent, scalable monitoring of livestock infrastructure, enabling risk modeling, change detection, and targeted regulatory action. Github: https://github.com/Nibir088/PRISM-CAFO.