Accurate automated photo-identification of Near Eastern and European fire salamanders
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.
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BibTeX
@misc{yesharim2026accurate,
title = {{Accurate automated photo-identification of Near Eastern and European fire salamanders}},
author = {Maayan Yesharim and Hava Goldstein and Gabi Kolodny and Assaf Ben-Levy and Tamar Kis-Papo and Dor Gavish and Rotem Vidan and Nirit Lavie-Alon and Olga Rybak and Yoav Ram},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.01601},
eprint = {2611.01601},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.01601},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Accurate automated photo-identification of Near Eastern and European fire salamanders
AU - Maayan Yesharim
AU - Hava Goldstein
AU - Gabi Kolodny
AU - Assaf Ben-Levy
AU - Tamar Kis-Papo
AU - Dor Gavish
AU - Rotem Vidan
AU - Nirit Lavie-Alon
AU - Olga Rybak
AU - Yoav Ram
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.01601
UR - https://arxiv.org/abs/2611.01601
AB - 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.
ER -