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Ali Mansourian

Ali Mansourian

Professor

Ali Mansourian

ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models

Author

  • Ali Mansourian
  • Rachid Oucheikh

Summary, in English

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis.

Department/s

  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • Centre for Geographical Information Systems (GIS Centre)
  • Dept of Physical Geography and Ecosystem Science

Publishing year

2024-10

Language

English

Publication/Series

ISPRS International Journal of Geo-Information

Volume

13

Issue

10

Document type

Journal article

Publisher

MDPI AG

Topic

  • Earth and Related Environmental Sciences
  • Computer and Information Science

Keywords

  • Large Language Models (LLMs)
  • Generative AI
  • natural language processing (NLP)
  • Code generation
  • Geospatial Artificial Intelligence (GeoAI)
  • Llama
  • Spatial analysis
  • Geographic Information System (GIS)
  • GIS democratization

Status

Published

ISBN/ISSN/Other

  • ISSN: 2220-9964