Autonomous Agentic AI for Geospatial Search: Do LLMs Outperform Traditional GIS Models?

The integration of Artificial Intelligence (AI) into geospatial search has revolutionized the way we interact with geographic information systems (GIS). Traditional GIS models have long been the cornerstone of spatial data analysis, but the advent of Large Language Models (LLMs) has introduced a new paradigm in autonomous agentic AI. This paper investigates whether LLMs can outperform traditional GIS models in geospatial search tasks. We evaluate the performance of LLMs against conventional GIS models across various metrics, including accuracy, efficiency, scalability, and adaptability. Our findings suggest that while LLMs exhibit remarkable capabilities in natural language understanding and contextual reasoning, they are not yet fully capable of replacing traditional GIS models in all aspects. However, LLMs show significant promise in enhancing user interaction and providing more intuitive search experiences. This paper concludes with a discussion on the potential hybrid approaches that leverage the strengths of both LLMs and traditional GIS models