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

Abstract

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.

Introduction

Geospatial Search Evolution
Geospatial search is a core component of modern Geographic Information Systems (GIS), enabling users to query and analyze spatial data. Traditional GIS models use structured query languages, spatial indexing (e.g., R-trees), and algorithms for tasks like route planning and spatial joins. They are highly accurate and efficient but require technical expertise, which limits accessibility for non-expert users.

Role of Large Language Models (LLMs)
The emergence of Large Language Models (LLMs) like GPT-4 introduces a new, more accessible approach to geospatial search. LLMs can interpret natural language queries and generate relevant responses, making spatial data more user-friendly. However, their accuracy and efficiency in complex spatial tasks compared to traditional GIS models remain under evaluation.

Research Purpose
The paper systematically compares LLMs and traditional GIS models across four key dimensions: accuracy, efficiency, scalability, and adaptability. It also explores hybrid systems that combine the strengths of both technologies and addresses gaps in the literature, such as limited head-to-head evaluations and ethical concerns (e.g., data privacy).

Datasets and Methodology
The study uses multiple datasets, including:

• OverpassNL (natural language to OverpassQL queries),

• Geospatial Text Queries (natural queries like “find the nearest coffee shop”),

• BIRD (complex text-to-SQL queries),

• TCQL (text-to-Corpus Query Language for linguistics analysis).

Performance metrics include accuracy (relevance and correctness), efficiency (response time), scalability (handling larger datasets), and adaptability (handling diverse queries).

Results

• Traditional GIS Models outperform LLMs in precise spatial computations (e.g., 98% vs. 85% accuracy in nearest neighbor tasks).

• LLMs excel in natural language understanding, contextual reasoning, and generating human-readable summaries.

• Hybrid approaches are promising, allowing LLMs to handle user interaction and traditional GIS models to process structured spatial data.

Conclusion

This study has provided a comprehensive evaluation of the performance of Large Language Models (LLMs) and traditional GIS models in geospatial search tasks. Our findings reveal that both approaches have distinct strengths and limitations, and their effectiveness varies depending on the specific task and context. A. Key Findings 1) Accuracy: Traditional GIS models outperformed LLMs in tasks requiring precise spatial calculations, such as nearest neighbor search and route planning. However, LLMs demonstrated superior performance in tasks requiring natural language understanding and contextual reasoning, such as spatial data summarization. 2) Efficiency: Traditional GIS models were significantly faster than LLMs in processing spatial queries, thanks to their optimized spatial indexing and query processing algorithms. However, LLMs were more efficient in processing natural language queries, providing a more user-friendly interface for non-expert users. 3) Scalability: Traditional GIS models demonstrated superior scalability, handling large-scale datasets and complex queries with ease. LLMs struggled with scalability, particularly when the dataset size or query complexity increased. 4) Adaptability: LLMs showed greater adaptability in tasks requiring natural language understanding and contextual reasoning, while traditional GIS models were more adaptable to different types of spatial datasets and queries. B. Implications The findings of this study have important implications for the future of geospatial search. While LLMs are not yet capable of fully replacing traditional GIS models in all aspects, they show significant promise in enhancing user interaction and providing more intuitive search experiences. This suggests that hybrid approaches, which leverage the strengths of both LLMs and traditional GIS models, could play a crucial role in the evolution of geospatial search systems. For example, a hybrid system could use LLMs to interpret natural language queries and generate structured queries for traditional GIS models. This would combine the natural language understanding capabilities of LLMs with the spatial query processing capabilities of traditional GIS models, resulting in a more powerful and user-friendly geospatial search system. Such a system could democratize access to geospatial information, making it more accessible to a broader audience. The integration of LLMs into geospatial search represents a significant step forward in making spatial data more accessible and user-friendly. While traditional GIS models remain indispensable for tasks requiring precise spatial calculations, LLMs offer exciting possibilities for enhancing user interaction and providing more intuitive search experiences. By leveraging the strengths of both approaches, we can develop geospatial search systems that are not only powerful and efficient but also accessible to a broader audience.

References

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Copyright

Copyright © 2025 Deepali Ahir, Shishir N, Kaushal B., Aditya S., Sunil Kumawat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.