Accurate photo ID helps track fire salamanders without training data
Accurate automated photo-identification of Near Eastern and European fire salamanders
Computer Vision and Pattern Recognition
Summary
Keeping track of individual fire salamanders through photos can be slow and prone to mistakes as the number of pictures grows. The authors created a method that spots unique yellow patterns on salamanders using smart feature-matching techniques without needing to teach the computer species details. Tested on thousands of salamander photos in Israel and Europe, it correctly matched individual salamanders over 95% of the time and worked well even on related species. This approach could make monitoring these amphibians easier, faster, and less error-prone.
What this means in practice
- •For wildlife monitoring teams: Automatically identify individual fire salamanders to improve non-invasive population studies without needing species-specific training data.
- •For conservation project managers: Audit and correct large amphibian photo ID databases to reduce errors and improve capture-recapture analysis accuracy.
Authors
Maayan Yesharim, Hava Goldstein, Gabi Kolodny, Assaf Ben-Levy, Tamar Kis-Papo, Dor Gavish, Rotem Vidan, Nirit Lavie-Alon, Olga Rybak, Yoav Ram
Abstract
Photo-identification underpins non-invasive capture-recapture monitoring of amphibians, but manual identification becomes slow and error-prone as reference libraries grow. Focusing on the Near Eastern fire salamander (Salamandra infraimmaculata), we developed a pipeline based on zero-shot local-feature matching (SuperPoint and LightGlue) without species-specific training and evaluated it on $2{,}440$ field photographs from Haifa and Tel Dan in northern Israel. We isolated the yellow spot pattern, scored image similarity by the number of high-confidence keypoint matches, which reduced a confound whereby many-spotted individuals accumulate spurious matches, and assigned identities across the database by two-tier clustering. Pipeline-assisted expert review corrected misidentifications, reducing the number of identities by almost a quarter. On held-out individuals, the pipeline ranked the correct individual first for $95.2\%$ of queries and recovered identities with an ARI of $0.958$. Without modification, it reached $95.1\%$ and $91.7\%$ top-1 accuracy on two datasets of the European fire salamander (Salamandra salamandra) and recognised $93.7\%$ of recaptures in one of them, compared with $38$-$77\%$ for five established programs and $93.9\%$ for visual inspection. Zero-shot local-feature matching thus offers amphibian monitoring a re-identification tool that needs no training data, transfers between related species, scales to databases of over a thousand individuals, and can audit existing databases before capture-recapture analysis.