Fine-tuning LLMs for spatial analysis and modeling: An initial step

Geospatial Information Science (GIScience) is a multidisciplinary field focused on developing methods for managing and analyzing geographic data, which are essential for understanding the spatial and temporal dynamics of natural and human-made processes. These analyses often require the creation of complex models within Geographic Information Systems (GIS). However, building such models typically demands specialized, modeling-specific knowledge, making them accessible primarily to GIS professionals. To address this barrier, we leverage the emerging capabilities of Large Language Models (LLMs) to democratize GIS modeling. As part of this effort, we developed and deployed IntelliGeo, a QGIS plugin that functions as an AI-powered modeling assistant. Since no current LLM is fine-tuned for spatial analysis and modeling, IntelliGeo enhances the capabilities of general-purpose LLMs through two mechanisms: Retrieval-Augmented Generation (RAG) to supply GIS-specific information to LLMs, and Few-Shot Learning to provide examples of prompts for generating geodata processing models along with their expected outputs. By iteratively capturing and refining user inputs, IntelliGeo not only simplifies modeling efforts but also contributes to building a dataset for fine-tuning LLMs for spatial analysis and modeling. In this poster, we illustrate IntelliGeo, with special emphasis on the RAG mechanism.