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

Watershed Management Laboratory, Faculty of Forestry, Universitas Gadjah Mada, Indonesia

Abstract

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.