Towards intelligent geospatial data discovery: a knowledge graph-driven multi-agent framework powered by large language models

Abstract

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 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 this challenge, 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, it adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer synthesis, forming an interpretable and closed-loop discovery process. Results show that the framework substantially improves ranking quality and recall compared with traditional systems with high intent matching accuracy and discovery transparency. This study demonstrates the potential to advance geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, shedding light on the development of next-generation intelligent and autonomous spatial data infrastructures.