Recent advancements in Generative AI offer promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. In this study, we propose a framework for integrating LLMs directly into existing GIS platforms, using QGIS as an example. Our approach leverages the reasoning and programming capabilities of LLMs to autonomously generate spatial analysis workflows and code through an informed agent that has comprehensive documentation of key GIS tools and parameters. The implementation of this framework resulted in the development of a "GIS Copilot" that allows GIS users to interact with QGIS using natural language commands for spatial analysis. The GIS Copilot was evaluated with over 100 spatial analysis tasks with three complexity levels: basic tasks that require one GIS tool and typically involve one data layer to perform simple operations; intermediate tasks involving multi-step processes with multiple tools, guided by user instructions; and advanced tasks which involve multi-step processes that require multiple tools but not guided by user instructions, necessitating the agent to independently decide on and executes the necessary steps. The evaluation reveals that the GIS Copilot demonstrates strong potential in automating foundational GIS operations, with a high success rate in tool selection and code generation for basic and intermediate tasks, while challenges remain in achieving full autonomy for more complex tasks. This study contributes to the emerging vision of Autonomous GIS, providing a pathway for non-experts to engage with geospatial analysis with minimal prior expertise. While full autonomy is yet to be achieved, the GIS Copilot demonstrates significant potential for simplifying GIS workflows and enhancing decision-making processes.

翻译：生成式人工智能的最新进展为空间分析提供了前景广阔的能力。尽管潜力巨大，但生成式人工智能与成熟地理信息系统平台的整合仍处于探索不足的阶段。在本研究中，我们提出了一个将大语言模型直接集成到现有地理信息系统平台的框架，并以QGIS为例进行说明。我们的方法利用大语言模型的推理和编程能力，通过一个具备关键地理信息系统工具与参数完整文档知识的智能代理，自主生成空间分析工作流和代码。该框架的实施催生了"GIS Copilot"的开发，使得地理信息系统用户能够通过自然语言指令与QGIS交互以执行空间分析。GIS Copilot在超过100项空间分析任务中进行了评估，这些任务分为三个复杂度等级：基础任务仅需单一地理信息系统工具且通常涉及单数据层以执行简单操作；中级任务涉及多步骤流程并需使用多种工具，由用户指令引导；高级任务同样涉及多步骤流程且需多种工具，但无需用户指令引导，要求代理自主决策并执行必要步骤。评估结果表明，GIS Copilot在基础地理信息系统操作自动化方面展现出强大潜力，在基础和中級任务中实现了较高的工具选择与代码生成成功率，但在实现更复杂任务的完全自主性方面仍面临挑战。本研究为新兴的自主地理信息系统愿景作出贡献，为非专业人士以最低的先验专业知识参与地理空间分析提供了可行路径。虽然完全自主性尚未实现，但GIS Copilot在简化地理信息系统工作流和增强决策过程方面展现出显著潜力。