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Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification — arXiv:2608.02762

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.02762
License (as archived): CC0-1.0
Archived: 2026-10-31T12:58:02.896Z
SHA-256 of the archived text: 5f830030e6114019267f2bc944499fc67b73b2eb36e0d1be0944a8ee38310711
Stable URL of this page: https://metraipolis.com/documento/dc_2ceed727031c

How to cite

Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification — arXiv:2608.02762 — arXiv (en), https://arxiv.org/abs/2608.02762 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ceed727031c (Metraipolis archive dc_2ceed727031c, sha256 5f830030e6114019267f2bc944499fc67b73b2eb36e0d1be0944a8ee38310711)

BibTeX

@misc{dc2ceed727031c,
  title = {Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification — arXiv:2608.02762},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.02762},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ceed727031c; sha256 5f830030e6114019267f2bc944499fc67b73b2eb36e0d1be0944a8ee38310711},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.02762. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seasonally, and modality-specific evidence can be ambiguous. We study adult-male identification in red deer ($\textit{Cervus elaphus}$), where the antler cycle defines predictable windows of reliable evidence for both annotation and prediction. Using 7,295 RGB-only, thermal-only, and matched RGB+thermal crop sets from low-altitude UAV surveys, labeled by three annotators, we show that seasonal structure links (I) annotation quality, (II) downstream classification, and (III) selective prediction. Matched RGB+thermal review resolves more samples than either single modality, recovering majority-male labels otherwise missed by RGB or thermal alone, in human-based as well as model-based classification. Months with high annotator abstention also show lower classifier confidence, and soft seasonal priors mainly benefit the season-limited thermal view. Uncertainty-band abstention further raises covered accuracy to 98.9%, though at reduced coverage and with deferral that falls disproportionately on males. Overall, a biologically grounded seasonal calendar predicts where annotation and prediction are unreliable, and can guide both annotation protocol design and modality weighting.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-05 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.02762 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:45:33.195Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).