AI and Image Processing Applications for Wildlife Surveillance and Habitat Mapping in Thar Desert

Abstract

This study explored the application of Artificial Intelligence (AI) and image processing technologies for wildlife surveillance and habitat mapping in the Thar Desert, a fragile and biodiverse arid ecosystem. The research aimed to enhance species detection, habitat assessment, and migration prediction through advanced machine learning and deep learning models. Using drone imagery, satellite data, and automated recognition frameworks such as YOLOv8, U-Net, and CNN-LSTM, the study analyzed large datasets to identify wildlife species, classify vegetation types, and forecast movement patterns. Results demonstrated that AI-based models significantly improved detection precision, mapping accuracy, and predictive reliability compared to traditional ecological monitoring methods. YOLOv8 achieved the highest species identification accuracy, while U-Net provided superior habitat classification performance. The hybrid CNN-LSTM model effectively predicted migration routes and ecological corridors, contributing to improved conservation planning. The findings highlighted that AI integration not only optimized real-time monitoring but also minimized human interference, reduced survey costs, and increased data consistency. The study concluded that AI-driven ecological monitoring systems are vital for sustainable biodiversity management and can guide future policies for protecting arid ecosystems like the Thar Desert.