wi-cafo-analysis

Analysis code for detecting and characterizing dairy Concentrated Animal Feeding Operations (CAFOs) in Wisconsin using satellite imagery segmentation models and government permit data.

Research questions:

• How well can an image segmentation model detect dairy CAFOs and predict their barn footprints?
• How well can we predict dairy cattle herd size using barn footprint predictions?
• Can we use these results to identify potentially unpermitted dairy CAFOs?

Data

All data required to reproduce the paper results are available on Hugging Face at reglab/wisconsin-dairy-cafo.

Model weights and training/inference code are available in the cafo-segmentation repository.

Setup

Requirements: Python 3.11

1. Clone this repository.

2. Copy config/config_example.yml to config/config.yml and update each path to match your local filesystem.

3. Download the data from reglab/wisconsin-dairy-cafo (see below for directory structure).

4. Create a virtual environment and install dependencies:

uv venv --python 3.11 wicafo
uv pip install -r requirements.txt --python wicafo/bin/python3.11
source wicafo/bin/activate

Any environment manager targeting Python 3.11 works; replace the uv commands with your preferred tool.

Expected data directory layout

After downloading from Hugging Face, your data/ and analysis_outputs/ directories should look like:

data/
├── Annotations/
│   ├── full_state_cf_annotations.geojson
│   └── ewg_region_train_labels.geojson
├── WDNR_permitted_CAFOs/
│   └── WDNR_CAFOs.geojson
├── ewg_AFOs_012022.geojson
├── permit_animal_type_records_*.csv
├── milk_producers.geojson
├── County_Boundaries_24K/
├── WI_urban_areas/
└── water_data/

analysis_outputs/
└── Clusters/
    ├── all_CF_clusters.csv
    ├── all_cf_clusters.geojson
    ├── four_band_clusters.csv
    ├── three_band_clusters.csv
    └── hyper_parameter_best_model_clusters.csv

Large ancillary datasets not included in the HF repository:

• Wisconsin land parcels (V8.0.0): WI SCO
• NAIP imagery: stored on Google Cloud (GCP credentials required; contact the authors)
• SNAPMAPs nutrient management layers: DATCP open data portal

Reproducing paper results

python generate_paper_results.py

This generates all paper figures and tables in paper_results/:

Subfolder	Contents
01_model_validation/	Permit vs. AU estimate comparisons
02_segmentation_quality/	Pixel-level P/R/IOU curves, annotation error distributions
03_error_analysis/	Facility-level P/R curves, case study imagery (requires GCP)
04_unpermitted_analysis/	Statewide CAFO map, permit rate by size, size distributions
05_risk_assessment/	Multi-factor risk indices by animal unit category
tables/	CSV tables for publication
publication_dataset/	Final publication-ready facility dataset

Optional flags:

python generate_paper_results.py --skip-pixel-stats   # skip recomputing pixel P/R (use cached)
python generate_paper_results.py --skip-snapmaps      # skip SNAPMAPs layers (if not downloaded)
python generate_paper_results.py --skip-imagery       # skip NAIP tile case-study figures (no GCP needed)

Repository contents

File	Description
generate_paper_results.py	Master pipeline — runs all analyses and generates all figures
create_figures.py	Analysis functions called by the pipeline
plotting_utils.py	Plotting and visualization utilities
cluster.py	Polygon clustering from model predictions or human labels
cluster_analysis_functions.py	Cluster-level analysis (water risk, parcel matching, etc.)
estimate_animal_units.py	Animal unit estimation with uncertainty quantification
estimate_methane_emissions.py	Methane emissions estimation from dairy cattle counts
analyze_model_outputs.py	Model output loading and processing
process_snapmaps.py	Load/process SNAPMAPs geodatabase layers
naip_utils.py	NAIP imagery utilities (requires GCP credentials)
add_asym.py	Asymmetric uncertainty calculations
config/config_params.py	Global constants (EPSG codes, AU factors, figure styling)
config/config_example.yml	Configuration template — copy to config.yml and edit paths

Citation

Citation to be added upon publication.