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Recent advancements in generative artificial intelligence (AI), particularly Large Language Models (LLMs), offer promising capabilities for spatial analysis. However, their integration with established GIS platforms remains underexplored. In this study, we propose a framework that embeds LLMs into existing GIS platforms, using QGIS as a case study. Our approach leverages LLMs’ reasoning and coding abilities to autonomously generate spatial analysis workflows through an informed agent equipped with comprehensive documentation of key GIS tools and parameters. External tools such as GeoPandas are also incorporated to enhance the system's geoprocessing capabilities. Based on this framework, we developed a ‘GIS Copilot’ that enables users to interact with QGIS using natural language. We evaluated the copilot across over 100 tasks of varying complexity including basic (single tool/layer), intermediate (multistep with guidance), and advanced (multistep without guidance). Results show high success rates for basic and intermediate tasks, with challenges remaining in fully autonomous execution of advanced tasks. The GIS Copilot advances the vision of autonomous GIS by enabling non-experts to perform geospatial analysis with minimal prior knowledge. While full autonomy is not yet achieved, the copilot demonstrates significant potential for simplifying GIS workflows and enhancing decision-making processes.
Akinboyewa et al. (Mon,) studied this question.