# Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models

arXiv: https://arxiv.org/abs/2603.20670 · HTML: https://mlsanity.com/paper/2603.20670/

## Key facts

- Cited by 1 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 52 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## 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)

- [Autonomous GIS: the next-generation AI-powered GIS](https://mlsanity.com/paper/2305.06453/) (2023)
- [GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis](https://mlsanity.com/paper/2411.03205/) (2024)
- [An Autonomous GIS Agent Framework for Geospatial Data Retrieval](https://mlsanity.com/paper/2407.21024/) (2024)
- [GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS](https://mlsanity.com/paper/2503.23633/) (2025)
- [Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges](https://mlsanity.com/paper/1511.03010/) (2015)
- [Geospatial Big Data: Survey and Challenges](https://mlsanity.com/paper/2404.18428/) (2024)
- [CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation](https://mlsanity.com/paper/2505.09936/) (2025)
- [Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online](https://mlsanity.com/paper/2003.06561/) (2020)
- [From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL](https://mlsanity.com/paper/2510.21045/) (2025)

## Built on by (most cited first, 1 of 1)

- [Bringing Agentic Search to Earth Observation Data Discovery](https://mlsanity.com/paper/2607.02387/) (2026)

## Abstract (arXiv)

The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures still rely largely on keyword-based search with limited semantic support, which often fails to capture user intent and leads to weak retrieval performance. To address these challenges, this study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery, powered by large language models. The framework introduces a unified geospatial metadata ontology as a semantic mediation layer to align heterogeneous metadata standards across platforms and constructs a geospatial metadata knowledge graph to explicitly model datasets and their multidimensional relationships. Building on the structured representation, the framework adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer synthesis, forming an interpretable and closed-loop discovery process from user queries to results. Results from representative use cases and performance evaluation show that the framework substantially improves intent matching accuracy, ranking quality, recall, and discovery transparency compared with traditional systems. This study advances geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, providing a practical foundation for next-generation intelligent and autonomous spatial data infrastructures and contributing to the broader vision of Autonomous GIS.