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 based on 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极具前景的能力。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。本研究提出一个将大语言模型直接集成到现有GIS平台的框架，并以QGIS为例进行实现。我们的方法利用大语言模型的推理与编程能力，通过一个掌握关键GIS工具与参数完整文档的智能体，自主生成空间分析工作流及代码。该框架的实施最终开发出"GIS Copilot"系统，使GIS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的评估基于三个复杂度层级：基础任务（需单个GIS工具且通常涉及单一数据层执行简单操作）、中级任务（涉及多工具多步骤流程并由用户指令引导）以及高级任务（需多工具多步骤流程但无用户指令引导，要求智能体自主决策并执行必要步骤）。评估结果表明，GIS Copilot在基础GIS操作自动化方面展现出强大潜力，在基础与中级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，为非专业人员提供了以最低先验专业知识参与地理空间分析的途径。虽然完全自主性尚未实现，但GIS Copilot在简化GIS工作流程和增强决策过程方面展现出显著潜力。