Research overview: active learning and human-in-the-loop image labeling

Because labeling camera-trap images is costly and pretrained models falter on new projects, active-learning and human-in-the-loop workflows aim to maximize model improvement per unit of human effort. This overview surveys how these systems strategically select the most informative images for experts to annotate, retrain iteratively, and route uncertain predictions to humans while letting confident ones pass automatically. It discusses confidence thresholds, the division of labor between machine and human, and how such collaboration adapts classifiers efficiently to unfamiliar species and environments. The overview highlights that the practical bottleneck in camera-trap AI is rarely raw model capability but the production of adequate labeled data for each new context, and that intelligent allocation of scarce expert attention, rather than full automation, is often the most effective path to accurate, project-specific classification.