Advances in Wildlife Monitoring: Remote Sensing, Camera Traps, and Machine Learning Applications

The one-to-one care of wildlife inhabitants and their behavior is crucial for conservation, especially in the face of accelerating habitat loss, climate change, and human‑wildlife conflict. Traditional monitoring methods are often labor‑intensive, limited spatially, and lack real‑time capabilities. In recent years, technologies such as remote sensing, camera traps, drones, bio‑loggers, passive acoustic monitoring, and machine learning (ML) algorithms have significantly improved the precision, scale, and speed of wildlife monitoring. Globally, these tools are being used to monitor population density, spatial movement, behavior, and habitat condition; while in Pakistan, pilot projects—particularly those involving AI‑based camera traps—are showing promise in reducing conflict and improving detection. This review examines recent digital advances (up to mid-2025), compares global and Pakistani implementations, discusses technical, environmental, and ethical challenges, and suggests future directions for upgraded wildlife monitoring.