An Autonomous GIS Agent Framework for Geospatial Data Retrieval
Huan Ning, Zhenlong Li, Temitope Akinboyewa, M. Naser Lessani

TL;DR
This paper introduces an autonomous GIS agent framework powered by large language models that can discover, select, and retrieve geospatial data from multiple sources, enhancing automation in spatial analysis tasks.
Contribution
It presents a novel, flexible framework for autonomous geospatial data retrieval using LLMs, with a prototype implementation as a QGIS plugin and Python program.
Findings
Successfully retrieves data from diverse sources including OSM, US Census, ESRI, and others.
Demonstrates flexibility and extensibility through plug-and-play data source integration.
First autonomous geospatial data retrieval agent developed and tested.
Abstract
Powered by the emerging large language models (LLMs), autonomous geographic information systems (GIS) agents have the potential to accomplish spatial analyses and cartographic tasks. However, a research gap exists to support fully autonomous GIS agents: how to enable agents to discover and download the necessary data for geospatial analyses. This study proposes an autonomous GIS agent framework capable of retrieving required geospatial data by generating, executing, and debugging programs. The framework utilizes the LLM as the decision-maker, selects the appropriate data source (s) from a pre-defined source list, and fetches the data from the chosen source. Each data source has a handbook that records the metadata and technical details for data retrieval. The proposed framework is designed in a plug-and-play style to ensure flexibility and extensibility. Human users or autonomous data…
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Taxonomy
TopicsGeographic Information Systems Studies · Advanced Computational Techniques and Applications · Data Management and Algorithms
