Caribou Aerial Patches — OWL Benchmark Dataset

Point-annotated 512×512 px aerial image patches for caribou detection and counting from overhead survey imagery. This dataset accompanies the OWL paper and enables reproducible evaluation of point-based object detectors on aerial wildlife imagery. Train split (PCH 2017): 23,517 patches (18,322 annotated with 273,268 point annotations + 5,195 background controls) from the Porcupine Caribou Herd, Alaska. Test split (CAH 2022): 2,607 patches (1,852 annotated with 12,456 point annotations + 755 background controls) from the Central Arctic Herd, Alaska. This is a strict cross-herd and cross-temporal generalization benchmark: models trained on PCH 2017 are evaluated on CAH 2022 without any per-deployment retraining. Also includes pre-trained HerdNet (DLA-34) weights that reproduce the paper headline (F1 = 0.965 at τ = 20 px, c* = 0.20 on the test split). Contents: test.zip — 2,607 test patches (512×512 PNG) + gt.csv (12,456 annotations) train.zip — 23,517 training patches (512×512 PNG) + gt.csv (273,268 annotations) weights.zip — Pre-trained HerdNet best_model.pth (DLA-34, epoch 14, val F1 = 0.937) README.md — Dataset documentation, annotation format, benchmark results