Artificial Intelligence for Aerial Image Detection in Watershed Monitoring: A Case Study of Tamansari Catchment, Indonesia

Accurate land use information is vital for effective watershed monitoring and management. This study explores the use of ChatGPT-4o, a multimodal large language model (LLM), to interpret UAV-derived orthomosaics in the Tamansari Catchment, Central Java, Indonesia. High-resolution imagery from 2018 and 2025 was analyzed through natural language prompts to identify land use types and detect changes over time. Results revealed a significant shift toward intensive agriculture, with agroforestry decreasing from 32.3% to 4.8% and secondary forest cover halving from 19.4% to 9.7%. A hybrid validation strategy was applied, combining internal spatial consistency checks with external visual verification using Google Street View. While the method does not produce pixel-based classification maps, it enables descriptive interpretation without requiring advanced technical skills. The findings demonstrate that ChatGPT-4o can serve as a rapid, accessible, and cost-effective tool for participatory watershed monitoring, especially in data-scarce or low-resource environments. Further integration with ground-truth data is recommended to improve accuracy.