Large Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval. By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic information system (GIS) that leverages the LLM's general abilities in natural language understanding, reasoning, and coding for addressing spatial problems with automatic spatial data collection, analysis, and visualization. We envision that autonomous GIS will need to achieve five autonomous goals: self-generating, self-organizing, self-verifying, self-executing, and self-growing. We developed a prototype system called LLM-Geo using the GPT-4 API in a Python environment, demonstrating what an autonomous GIS looks like and how it delivers expected results without human intervention using three case studies. For all case studies, LLM-Geo was able to return accurate results, including aggregated numbers, graphs, and maps, significantly reducing manual operation time. Although still in its infancy and lacking several important modules such as logging and code testing, LLM-Geo demonstrates a potential path toward the next-generation AI-powered GIS. We advocate for the GIScience community to dedicate more effort to the research and development of autonomous GIS, making spatial analysis easier, faster, and more accessible to a broader audience.

翻译：大语言模型（LLM），如ChatGPT，展现出对人类自然语言的深刻理解，并已广泛应用于推理、创意写作、代码生成、翻译和信息检索等多个领域。通过采用LLM作为推理核心，我们提出自主式地理信息系统（Autonomous GIS），这是一种人工智能驱动的地理信息系统，它利用LLM在自然语言理解、推理和编码方面的通用能力，通过自动化的空间数据收集、分析和可视化来解决空间问题。我们设想自主式GIS需要实现五个自主目标：自我生成、自我组织、自我验证、自我执行和自我成长。我们利用GPT-4 API在Python环境中开发了一个原型系统LLM-Geo，通过三个案例研究展示了自主式GIS的形态以及其如何在无需人工干预的情况下交付预期结果。在所有案例研究中，LLM-Geo均能返回准确结果，包括聚合数字、图表和地图，显著减少了手动操作时间。尽管该系统仍处于初级阶段，缺少日志记录和代码测试等重要模块，但LLM-Geo展示了迈向下一代AI驱动GIS的潜在路径。我们呼吁地理信息科学界投入更多精力研究和发展自主式GIS，使空间分析更简便、更快速，并为更广泛的用户群体所使用。