  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.
