BrundageLab/synthetic_wildlife_health

Synthetic Wildlife Health: Camera Trap Imagery for Alopecia and Body Condition Screening

Dataset Summary

This dataset contains 553 synthetic camera trap images depicting alopecia (hair loss consistent with mange) and body condition deterioration in North American wildlife, along with paired visual question-answering annotations for health assessment tasks.

All images are AI-generated edits of real camera trap photographs sourced from iWildCam 2022. The generative pipeline applies controlled phenotype edits to the animal region while preserving the original background, lighting conditions, and camera-trap artifacts. Every image in this dataset passed scene-drift quality control, verifying that the background was not altered by the generative model.

This dataset is released alongside the paper:

Generating Synthetic Wildlife Health Data from Camera Trap Imagery: A Pipeline for Alopecia and Body Condition Training Data
David Brundage PhD, University of Wisconsin–Madison, School of Veterinary Medicine

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Intended Use

This dataset is intended as a screening data source, not a diagnostic tool. Images are labeled as "hair loss consistent with mange" rather than clinically diagnosed mange. Visual assessment from camera trap imagery cannot distinguish mange from lice, seasonal shedding, mechanical hair loss, or idiopathic follicular inactivity. The intended model output from a classifier trained on this data is a suspect flag for expert review.

Appropriate uses include:

• Training wildlife health screening classifiers
• Sim-to-real transfer experiments for camera trap health monitoring
• Benchmarking synthetic data generation pipelines for ecological applications

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Dataset Structure

Data Fields

Field	Type	Description
image	Image	Camera trap photograph with synthetic phenotype edit applied
question	string	Health assessment question about the animal
answer	string	Structured health assessment answer
species	string	Latin species name
variant	string	Phenotype variant: sham, mange_only, emaciated_only, severe_both
mange_label	string	Alopecia severity: M0 (healthy), M2 (moderate), M3 (severe)
bcs_label	string	Body condition score: B0 (healthy), B2 (moderate wasting), B3 (severe emaciation)
qc_method	string	QC path: day_pass, night_pass
qc_mae_raw	float	Raw mean absolute error (background pixels)
qc_mae_norm	float	Color-normalized MAE (per-channel DC offset corrected)
qc_ssim	float	Structural similarity index (SSIM) of background region

Phenotype Variants

Variant	Mange Label	BCS Label	Description
sham	M0	B0	Healthy negative control
mange_only	M2	B0	Moderate patchy hair loss with scaling and crusting
emaciated_only	M0	B2	Visible ribs, noticeable muscle wasting
severe_both	M3	B3	Severe extensive hair loss + pronounced skeletal landmarks

Species

Species	Common Name
Urocyon cinereoargenteus	Gray fox
Canis lupus	Gray wolf
Odocoileus virginianus	White-tailed deer
Procyon lotor	Raccoon

Data Splits

Split	Size
Train	~497 images (90%)
Test	~56 images (10%)

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Generation Pipeline

Base Image Curation

Base images were drawn from iWildCam 2022 using MegaDetector v4 for animal bounding box detection. Sampling prioritized center-frame animal placement (weight 0.6 center, 0.3 mid-frame, 0.1 edge/corner) and maintained stratified balance across species, season, and day/night conditions.

Phenotype Editing

Synthetic variants were generated using Gemini 3.1 Flash Image. Each edit prompt supplied the model with MegaDetector bounding box coordinates, species identity, severity descriptors, and explicit instructions to edit only the animal region while preserving the background pixel-identical. Lesion placement followed species-specific biology: for canids, sarcoptic mange progression (periocular → ear margins → elbows → flanks); for hoofstock, distribution excludes dorsal midline regions inaccessible to self-grooming.

Quality Control

Every image in this dataset passed scene-drift QC using a decoupled mask-then-score approach:

1. Sham pre-filter: The M0/B0 sham variant is generated first; base images where the sham fails QC are excluded entirely (~16% of bases filtered this way).
2. Mask construction: Pixel-difference thresholding + connected component labeling identifies changed regions, constrained to overlap the MegaDetector bounding box. Images where the change mask exceeds 70% of frame area are rejected as global re-renders.
3. Scene scoring: Computed on Gaussian-blurred background pixels (outside the animal mask):
   • Daytime: OR-gate passes if normalized MAE ≤ 7.0 or SSIM ≥ 0.85
   • Nighttime/IR: raw MAE ≤ 5.0

Overall QC pass rate: 83% (553 of 666 generated variants). Images failing QC are not included in this release.

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Pipeline Statistics

Metric	Value
Species represented	4
Base images processed	201
Total variants generated	666
QC-passing variants (this dataset)	553 (83%)
Sham pre-filter rejection rate	16%
Daytime pass rate	85%
Nighttime pass rate	81%

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Sim-to-Real Transfer Results

A classifier trained exclusively on this synthetic dataset and evaluated on real camera trap images of suspected health conditions achieved:

Model	AUROC	Balanced Accuracy
MLP (256 hidden units) + DINOv2 ViT-B/14	0.854	0.811
Linear probe + DINOv2 ViT-B/14	0.734	—

The real test set (N=70) consisted of 45 healthy iWildCam images held out from the base image pool and 25 community-reported suspected health condition images, controlling for species identity between classes. Suspect labels are community-reported, not clinically confirmed.

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Limitations

• Diagnostic ambiguity: Camera trap imagery cannot distinguish mange from lice, seasonal shedding, or idiopathic hair loss.
• Species-condition mismatch: Confirmed mange in white-tailed deer is rare in some regions; the model may flag shedding or mechanical hair loss, requiring per-region calibration.
• Posture confounds: Edits are applied to healthy-posture base images and cannot capture illness-related postural changes.
• Transfer evaluation scale: The real evaluation set is small (N=70); the 0.85 AUROC reflects feasibility, not production-ready performance.

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Citation

If you use this dataset, please cite:

@inproceedings{brundage2026synthetic,
  title     = {Generating Synthetic Wildlife Health Data from Camera Trap Imagery: A Pipeline for Alopecia and Body Condition Training Data},
  author    = {Brundage, David},
}

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License

This dataset is released under CC BY 4.0. Base images are derived from iWildCam 2022; please also consult the iWildCam dataset license when using this data.

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Contact

David Brundage PhD
University of Wisconsin–Madison, School of Veterinary Medicine
brundage2@wisc.edu