# An Autonomous GIS Agent Framework for Geospatial Data Retrieval

arXiv: https://arxiv.org/abs/2407.21024 · HTML: https://mlsanity.com/paper/2407.21024/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 13 Jul 2024.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Autonomous GIS: the next-generation AI-powered GIS](https://mlsanity.com/paper/2305.06453/) (2023)
- [GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT](https://mlsanity.com/paper/2307.07930/) (2023)
- [Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages](https://mlsanity.com/paper/2310.09340/) (2023)
- [Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap](https://mlsanity.com/paper/2308.16060/) (2023)
- [Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks](https://mlsanity.com/paper/2402.03494/) (2024)
- [Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping](https://mlsanity.com/paper/2402.16684/) (2024)

## Built on by (most cited first, 26 of 26)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents](https://mlsanity.com/paper/2503.24047/) (2025)
- [GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis](https://mlsanity.com/paper/2411.03205/) (2024)
- [An LLM Agent for Automatic Geospatial Data Analysis](https://mlsanity.com/paper/2410.18792/) (2024)
- [Space3D-Bench: Spatial 3D Question Answering Benchmark](https://mlsanity.com/paper/2408.16662/) (2024)
- [Accelerating Earth Science Discovery via Multi-Agent LLM Systems](https://mlsanity.com/paper/2503.05854/) (2025)
- [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025)
- [GeoAnalystBench: A GeoAI benchmark for assessing large language models for spatial analysis workflow and code generation](https://mlsanity.com/paper/2509.05881/) (2025)
- [ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing](https://mlsanity.com/paper/2410.12376/) (2024)
- [CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications](https://mlsanity.com/paper/2512.15231/) (2025)
- [GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis](https://mlsanity.com/paper/2604.13888/) (2026)
- [CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation](https://mlsanity.com/paper/2505.09936/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives](https://mlsanity.com/paper/2602.21351/) (2026)
- [Towards a Barrier-free GeoQA Portal: Natural Language Interaction with Geospatial Data Using Multi-Agent LLMs and Semantic Search](https://mlsanity.com/paper/2503.14251/) (2025)
- [The Role of Open-Source LLMs in Shaping the Future of GeoAI](https://mlsanity.com/paper/2504.17833/) (2025)
- [From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL](https://mlsanity.com/paper/2510.21045/) (2025)
- [GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?](https://mlsanity.com/paper/2505.10714/) (2025)
- [The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features](https://mlsanity.com/paper/2506.00203/) (2025)
- [Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models](https://mlsanity.com/paper/2603.20670/) (2026)
- [Fine-Tuning Small Language Models (SLMs) for Autonomous Web-based Geographical Information Systems (AWebGIS)](https://mlsanity.com/paper/2508.04846/) (2025)
- [Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications](https://mlsanity.com/paper/2601.06056/) (2025)
- [GISclaw: A Comprehensive Open-Source LLM Agent System for Realistic Multi-Step Geospatial Analysis](https://mlsanity.com/paper/2603.26845/) (2026)
- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)
- [Broadening Access to Transportation Safety Data with Generative AI: A Schema-Grounded Framework for Spatial Natural Language Queries](https://mlsanity.com/paper/2605.21712/) (2026)
- [GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks](https://mlsanity.com/paper/2610.12669/) (2026)

## Abstract (arXiv)

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.