让AI真正理解空间关系，用科学理论生成可执行的地理分析流程
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts
- 将地理问题转为带逻辑顺序的空间概念图谱，实现精准推理
- 在两个基准测试中显著超越现有方法，准确率提升超20%
- 适合城市规划、灾害响应等需要真实空间计算的场景
地理空间推理对城市分析、交通规划和灾害响应等实际应用至关重要。然而，现有基于大模型的智能体常无法进行真正的空间计算，依赖网络搜索或模式匹配，且容易虚构空间关系。我们提出Spatial-Agent，一种基于空间信息科学基础理论的AI代理。该方法将地理分析问答问题形式化为概念转换问题，将自然语言问题解析为可执行的工作流，以GeoFlow图（有向无环图）表示，节点对应空间概念，边代表变换操作。基于空间信息理论，Spatial-Agent提取空间概念，赋予其功能角色并施加合理排序约束，通过模板生成组合变换序列。在MapEval-API和MapQA基准测试上的大量实验表明，Spatial-Agent显著优于现有基线方法（包括ReAct和Reflexion），同时生成可解释且可执行的地理工作流。
原文摘要 · Abstract (English)
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 sequences through template-based generation. Extensive experiments on MapEval-API and MapQA benchmarks demonstrate that Spatial-Agent significantly outperforms existing baselines including ReAct and Reflexion, while producing interpretable and executable geospatial workflows.
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