Large Language Model Agent with VGI Data for Mapping

In recent years, Volunteered Geographic Information (VGI) has emerged as a crucial source of mapping data, contributed by users through crowdsourcing platforms such as OpenStreetMap. This paper presents a novel approach that Integrates Large Language Models (LLMs) into a fully automated mapping workflow, utilizing VGI data. The process leverages Prompt Engineering, which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs. By constructing precise and detailed prompts, LLM agents are able to accurately interpret mapping requirements, and autonomously extract, analyze, and process VGI geospatial data. They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation. This approach significantly streamlines the creation of high-quality mapping outputs, reducing the time and resources typically required for such tasks. Moreover, the system lowers the barrier for non-expert users, enabling them to generate accurate maps without extensive technical expertise. Through various case studies, we demonstrate the LLM application across different mapping scenarios, highlighting its potential to enhance the efficiency, accuracy, and accessibility of map production. The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields, including urban planning and infrastructure development.