This research develops adaptable machine learning methods for wildlife monitoring using camera trap images. By clustering visually similar animal images, the system dramatically reduces the amount of manual labeling required while maintaining accuracy. The approach could enable faster, large-scale biodiversity monitoring critical for protecting endangered species worldwide.

Australia’s wildlife is hard to count due to difficult terrain and vast landscapes. This research uses remote sensing—camera traps, audio recorders, drones, and satellites—combined with AI and mathematical models to understand animal presence, habitat choices, and detectability. The goal is faster, more accurate population monitoring to guide conservation.