用多智能体LLM自动处理地理信息中的Shapefile数据
ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing
- 设计多智能体框架，分工规划与执行任务
- 95.24%成功率，优于GPT系列模型
- 适合无GIS背景的研究者快速上手空间分析
矢量数据是地理信息系统（GIS）的核心数据结构之一，用于精确存储和表达地理空间信息。Shapefile作为最广泛使用的矢量数据格式，已成为所有主流地理信息系统支持的行业标准。然而，处理此类数据通常需要专业的GIS知识与技能，对其他领域研究人员构成障碍，限制了空间数据分析的跨学科研究。尽管大语言模型（LLMs）在自然语言处理和任务自动化方面取得显著进展，但在处理地理信息系统中复杂的空间与拓扑关系方面仍面临挑战。为此，我们提出ShapefileGPT，一个基于大语言模型的创新框架，专为自动化处理Shapefile任务而设计。该框架采用多智能体架构，由规划智能体负责任务分解与监督，工作智能体执行具体操作。我们开发了专门的函数库以处理Shapefile，并提供完整的API文档，使工作智能体可通过函数调用高效操作数据。为评估性能，我们基于权威教材构建了一个基准数据集，涵盖几何运算与空间查询等任务类别。ShapefileGPT在任务成功率达到95.24%，优于GPT系列模型。相比传统大语言模型，ShapefileGPT能有效处理复杂矢量数据分析任务，克服了传统模型在空间分析中的局限性。这一突破为提升GIS领域的自动化与智能化水平开辟了新路径，在跨学科数据分析与应用中具有重要潜力。
原文摘要 · Abstract (English)
Vector data is one of the two core data structures in geographic information science (GIS), essential for accurately storing and representing geospatial information. Shapefile, the most widely used vector data format, has become the industry standard supported by all major geographic information systems. However, processing this data typically requires specialized GIS knowledge and skills, creating a barrier for researchers from other fields and impeding interdisciplinary research in spatial data analysis. Moreover, while large language models (LLMs) have made significant advancements in natural language processing and task automation, they still face challenges in handling the complex spatial and topological relationships inherent in GIS vector data. To address these challenges, we propose ShapefileGPT, an innovative framework powered by LLMs, specifically designed to automate Shapefile tasks. ShapefileGPT utilizes a multi-agent architecture, in which the planner agent is responsible for task decomposition and supervision, while the worker agent executes the tasks. We developed a specialized function library for handling Shapefiles and provided comprehensive API documentation, enabling the worker agent to operate Shapefiles efficiently through function calling. For evaluation, we developed a benchmark dataset based on authoritative textbooks, encompassing tasks in categories such as geometric operations and spatial queries. ShapefileGPT achieved a task success rate of 95.24%, outperforming the GPT series models. In comparison to traditional LLMs, ShapefileGPT effectively handles complex vector data analysis tasks, overcoming the limitations of traditional LLMs in spatial analysis. This breakthrough opens new pathways for advancing automation and intelligence in the GIS field, with significant potential in interdisciplinary data analysis and application contexts.
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