SatoyamaCT: A Multi-Axis Night-IR Camera-Trap Benchmark for Monitoring Crop-Damaging Wildlife in Japanese Agroforestry

arXiv:2609.30278 · eess.IV, q-bio.QM · Submitted 2026-08-19 · Read on arXiv

eess.IV, q-bio.QM

Submitted: 2026-08-19

Updated: 2026-08-19

Code: https://github.com/keito-git/satoyama-ct

Terminology

• Benchmark — 38× in this paper · explained in 2 episodes
• Accuracy — 29× in this paper · explained in 1 episode
• Generalization — 25× in this paper · explained in 13 episodes
• Calibration — 21× in this paper · explained in 7 episodes
• Embeddings — 13× in this paper · explained in 2 episodes
• Fine-Tuning — 13× in this paper · explained in 5 episodes
• Linear probe — 12× in this paper · explained in 2 episodes
• Domain Shift — 7× in this paper · explained in 4 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Distribution Shift — 4× in this paper · explained in 5 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Annotator Agreement — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Active Learning — 2× in this paper · explained in 6 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Deferral — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• The iWildCam 2021 Competition Dataset
• The iWildCam 2020 Competition Dataset
• Efficient Pipeline for Camera Trap Image Review
• Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
• Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time
• DINOv2: Learning Robust Visual Features without Supervision
• Learning Transferable Visual Models From Natural Language Supervision
• CATALOG: A Camera Trap Language-guided Contrastive Learning Model
• BioCLIP: A Vision Foundation Model for the Tree of Life
• AP-10K: A Benchmark for Animal Pose Estimation in the Wild

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