SGTAgent: An Agent Framework for Geospatial Target Localization from Complex Spatial Semantic Queries

Geospatial target localization refers to determining the geographic location of targets from natural language queries, which is essential for applications such as urban management, emergency response, and location-based services. As user queries evolve from address matching toward complex forms incorporating multiple spatial semantic factors, the localization process increasingly demands the coordination of heterogeneous tools and data sources. Automation remains hindered by the lack of a unified structured representation for semantic factors, the difficulty of translating complex spatial constraints into executable operations, and the unreliability of single-round planning and execution. This study proposes SGTAgent, an agent framework for geospatial target localization from complex spatial semantic queries. The parser transforms queries into structured spatial semantic factors, the planner organizes dependent spatial workflows, and the feedback mechanism distinguishes local parameter refinement from workflow restructuring. Experiments show that SGTAgent achieves higher localization accuracy than the evaluated generic baselines across multiple LLM backbones. Using DeepSeek-V4.1-Flash, SGTAgent achieves localization accuracies of 39.86%, 68.76%, and 81.88% at distance thresholds of 100, 300, and 500 m, respectively, outperforming the evaluated agents under the corresponding experimental configurations. On complex queries using GPT-4o, it achieves an LA@500m of 80.47%, compared with 70.12% for ReAct. Ablation experiments show that removing the complete Restate–Replan feedback mechanism reduces the 500 m localization accuracy from 81.88% to 74.83%, demonstrating the effectiveness of the execution feedback mechanism under the evaluated configuration. An independent evaluation of the parser achieves macro-F1 scores of 89.76% on the original benchmark and 89.62% on independently formulated queries, indicating consistent semantic factor extraction capability across different query formulations.