A Comprehensive Dataset of Factory Farms in California Compiled Using Computer Vision and Human Validation

TL;DR

This paper presents Cal-FF, a comprehensive dataset of factory farms in California, created using computer vision and human validation to improve accuracy and fill data gaps.

Contribution

The novel contribution is a near-complete, validated CAFO dataset for California with richer data than existing administrative records.

Findings

Cal-FF captures 98% of California's CAFOs as of 2017, identifying 222 previously unrecorded locations.

The dataset includes validated permit data, land parcel details, satellite imagery, and facility features.

The dataset offers significant potential for research and monitoring of CAFO impacts.

Abstract

Concentrated Animal Feeding Operations (CAFOs) house livestock at high densities for prolonged periods of time, posing substantial risks to environmental and human health. However, limited public information on CAFOs has constrained efforts to quantify their impacts on proximate communities. Gaps in permitting and reporting have severely limited studies that rely primarily on administrative records. We introduce Cal-FF, a near-complete census of CAFOs in California, a large and agriculturally significant state in the United States, with richer facility data than existing administrative data. Cal-FF was constructed using computer vision on satellite imagery, along with extensive human validation. We focus on California, which accounts for about 20% of US livestock production and has been at the forefront of CAFO regulatory innovation. We estimate that our 2,121 facility dataset captures 98% (95% CI [82%, 98%]) of all California CAFOs as of 2017, identifying 222 locations not present in state regulatory records. In addition to improved accuracy, Cal-FF adds a wealth of information about each facility, including validated permit information, land parcel data, satellite imagery, and annotated facility features. These data provide numerous opportunities for research, analysis, and monitoring.

Figures