GIS Copilot: towards an autonomous GIS agent for spatial analysis.
Uloženo v:
| Název: | GIS Copilot: towards an autonomous GIS agent for spatial analysis. |
|---|---|
| Autoři: | Akinboyewa, Temitope, Li, Zhenlong, Ning, Huan, Lessani, M. Naser |
| Zdroj: | International Journal of Digital Earth; Dec2025, Vol. 18 Issue 1, p1-23, 23p |
| Témata: | ARTIFICIAL intelligence, GEOGRAPHIC spatial analysis, GEOGRAPHIC information systems, CARTOGRAPHY software, INTELLIGENT agents, LANGUAGE models, HUMAN-computer interaction, GEOSPATIAL data |
| Abstrakt: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Digital Earth is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Databáze: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Buďte první, kdo okomentuje tento záznam!