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Ali Mansourian

Ali Mansourian

Researcher

Ali Mansourian

Bridging natural language and GIS : a multi-agent framework for LLM-driven autonomous geospatial analysis

Author

  • Ali Mansourian
  • Rachid Oucheikh

Summary, in English

Existing LLM-based approaches remain limited by simplistic task execution, restricted tool integration, and a lack of contextual reasoning when interacting with professional GIS software. This study investigates the efficacy of a multi-agent architecture designed to enhance geospatial task execution accuracy through collaboration, reasoning and tool-use. The architecture integrates Chain of Thought (CoT) reasoning and Retrieval-Augmented Generation (RAG) and employs specialized agents that collaboratively translate high-level natural language queries into structured, executable workflows using QGIS processing algorithms as tools. Through a structured fine-tuning approach, we evaluated how the multi-agent setup influences spatial task comprehension, geoprocessing tool selection, and code generation. The results demonstrate that the developed framework significantly outperforms baseline single-agent and non-fine-tuned systems. For tasks involving one or two GIS tools, the system achieved up to 100% execution success and 87.5% semantic correctness. However, its effectiveness decreases with more complex, multi-step workflows. Notably, iterative self-refinement and self-debugging led to moderate gains in execution success and semantic correctness. The results indicate that multi-agent frameworks are a promising approach, even though improvements in reasoning depth and tool alignment are still needed. This study represents a foundational step toward more reliable, modular, and adaptable LLM-based systems for geospatial analysis automation.

Department/s

  • Department of Earth and Environmental Sciences (MGeo)
  • LU Profile Area: Nature-based future solutions
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • Centre for Geographical Information Systems (GIS Centre)
  • Dept of Physical Geography and Ecosystem Science
  • eSSENCE: The e-Science Collaboration

Publishing year

2026

Language

English

Publication/Series

International Journal of Digital Earth

Volume

19

Issue

1

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Embedded Systems

Keywords

  • AI Agent
  • autonomous geospatial analysis
  • GeoAI
  • GIS
  • Large Languages Models (LLMs)

Status

Published

ISBN/ISSN/Other

  • ISSN: 1753-8947