Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification
arXiv:2608.02762v1 Announce Type: new Abstract: 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, 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 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 improves covered accuracy up 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.
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[Submitted on 3 Aug 2026]
Title:Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification
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Abstract: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, 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 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 improves covered accuracy up 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.
Comments: 17 pages, 4 figures, 4 tables. Accepted to the archival (proceedings) track of the CV4Ecology workshop at ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.4.8; I.5.4; J.3
Cite as: arXiv:2608.02762 [cs.CV]
(or arXiv:2608.02762v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.02762
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hugo Markoff [view email] [v1] Mon, 3 Aug 2026 18:05:47 UTC (646 KB)
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