Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts
Riyang Bao, Cheng Yang, Dazhou Yu, Zhexiang Tang, Gengchen Mai, Liang Zhao

TL;DR
Spatial-Agent introduces a novel geo-spatial reasoning framework grounded in spatial information science, transforming natural language questions into executable workflows for improved accuracy in geospatial tasks.
Contribution
It formalizes geo-analytical question answering as a concept transformation problem using GeoFlow Graphs, integrating spatial theories into AI reasoning.
Findings
Outperforms existing baselines like ReAct and Reflexion
Produces interpretable geospatial workflows
Demonstrates significant accuracy improvements on benchmarks
Abstract
Geospatial reasoning is essential for real-world applications such as urban analytics, transportation planning, and disaster response. However, existing LLM-based agents often fail at genuine geospatial computation, relying instead on web search or pattern matching while hallucinating spatial relationships. We present Spatial-Agent, an AI agent grounded in foundational theories of spatial information science. Our approach formalizes geo-analytical question answering as a concept transformation problem, where natural-language questions are parsed into executable workflows represented as GeoFlow Graphs -- directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. Drawing on spatial information theory, Spatial-Agent extracts spatial concepts, assigns functional roles with principled ordering constraints, and composes transformation…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Geographic Information Systems Studies · Data Management and Algorithms
