An Autonomous GIS Agent Framework for Geospatial Data Retrieval

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 scrawlers can add new data sources by adding new handbooks. We developed a prototype agent based on the framework, released as a QGIS plugin (GeoData Retrieve Agent) and a Python program. Experiment results demonstrate its capability of retrieving data from various sources including OpenStreetMap, administrative boundaries and demographic data from the US Census Bureau, satellite basemaps from ESRI World Imagery, global digital elevation model (DEM) from OpenTopography.org, weather data from a commercial provider, the COVID-19 cases from the NYTimes GitHub. Our study is among the first attempts to develop an autonomous geospatial data retrieval agent.