Multi-agent autonomous GeoAI framework for scalable and self-improving geospatial intelligence

Abstract

Large language models (LLMs) have recently expanded the scope of automation across many application domains. In geographic information systems (GIS), however, many tasks still require specialized expertise and remain difficult for non-expert users. Recent studies have explored LLM-based geospatial analysis under a single-agent paradigm, but these early systems remain limited by weak coordination, limited error recovery, and dependence on proprietary artifacts. This study proposes multi-agent autonomous geospatial artificial intelligence (MA-GeoAI), a multi-agent architecture in which the planner, coder, validator, debugger, and knowledge agents collaborate through the LangGraph framework. The framework was evaluated on three case studies: population exposure assessment, mobility pattern analysis, and county-level mortality modeling. Unlike general-purpose multi-agent LLM frameworks, MA-GeoAI embeds spatial semantics, coordinate reference system (CRS) consistency checks, geometry validation, and operation-aware coordination directly into the control loop. Across repeated runs, all evaluated systems completed the controlled artifact contract; therefore, the analysis focuses on auditability, runtime, fallback behavior, and reproducibility rather than binary task-completion superiority.