A Multilingual LLM-Based GeoAI Framework for Natural-Language-Driven Remote Sensing Analysis

Abstract. The exponential growth of remote sensing data in recent years has underscored the need for intelligent, fast, and user-friendly analytical tools. Despite advancements in platforms such as Google Earth Engine and ENVI, the computation of spectral indices still demands specialized expertise, considerable time, and complex parameter tuning. This study aims to reduce the complexity of spatial data analysis and enhance its accessibility for non-expert users by developing an intelligent system capable of transforming simple natural language commands into automated remote-sensing index calculations. The main innovation lies in integrating Large Language Models (LLMs) with geospatial processing to establish a lightweight, multilingual, and fully automated framework capable of identifying index types and selecting appropriate spectral bands from Landsat data. The system was implemented using the Bloomz-560m language model in combination with open-source image-processing engines and deployed as a web-based interface. Experimental results over Tehran demonstrated that the model outputs were highly consistent with those generated by Google Earth Engine and ENVI, achieving an RMSE of 0.016 and a correlation coefficient of R² = 0.957. The total processing time was under 45 seconds, with the entire workflow executed automatically without user intervention. By simplifying the analytical process and significantly reducing computation time, this framework represents a crucial step toward democratizing remote sensing and spatial analysis. It can be effectively applied to urban surface heat island (SUHI) monitoring, water resource management, and precision agriculture applications.