Deep Learning-Based Segmentation for Mapping Backyard Poultry in Canada
Bibliographic record
Abstract
Backyard poultry operations pose a potential risk of avian influenza transmission, a disease with severe economic consequences due to flock culling and trade restrictions. Existing surveillance efforts rely primarily on data from registered farms, often overlooking unregistered small-scale flocks that lack biosecurity and are more exposed to wild birds, a known reservoir for the virus. This creates a gap in the monitoring of avian influenza. This study proposes a deep learning-based approach specifically designed to detect backyard operations using high resolution satellite imagery. Although previous studies have applied satellite imagery and deep learning techniques to detect commercial poultry farms and large-scale livestock operations, these approaches have not been extended to backyard poultry detection. Our method addresses the challenge of identifying small, irregular and often unregistered backyard setups. We employ a fully convolutional network (FCN) with ResNet-50 backbone to perform binary semantic segmentation. The model achieved an accuracy of 81.13%, precision of 78.92%, recall of 84.96%, and an F1 score of 81.83%, outperforming other models on our dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".