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

Varun Magesh¹, Nicolas Rothbacher¹, Saskia Comess^1,2

¹Stanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA.

Scientific Data

|November 19, 2025

Summary

This summary is machine-generated.

A new dataset, Cal-FF, provides a comprehensive census of Concentrated Animal Feeding Operations (CAFOs) in California. This resource improves environmental and human health impact studies by offering richer facility data than previously available.

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Area of Science:

Environmental Science
Agricultural Science
Public Health

Background:

Concentrated Animal Feeding Operations (CAFOs) pose significant environmental and human health risks.
Limited public data on CAFOs hinders impact assessments on nearby communities.
Existing administrative records are insufficient due to gaps in permitting and reporting.

Purpose of the Study:

To create a near-complete census of California CAFOs with enhanced data.
To overcome limitations of administrative records for CAFO research.
To facilitate research and monitoring of CAFO impacts.

Main Methods:

Utilized computer vision on satellite imagery for CAFO identification.
Incorporated extensive human validation for data accuracy.
Focused on California, a major livestock producer and regulatory innovator.

Main Results:

Developed Cal-FF, a dataset of 2,121 CAFO facilities, capturing 98% of California CAFOs as of 2017.
Identified 222 CAFO locations not present in existing state regulatory records.
Cal-FF includes validated permit data, land parcel information, satellite imagery, and annotated features.

Conclusions:

Cal-FF offers a significantly improved resource for studying CAFOs compared to administrative data.
The dataset enhances accuracy and provides richer details for research and monitoring.
This comprehensive census supports better understanding and management of CAFO impacts.