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 including self-generating, self-organizing, self-verifying, self-executing, and self-growing. We developed a prototype system called LLM-Geo using GPT-4 API in a Python environment, demonstrating what an autonomous GIS looks like and how it delivers expected results without human intervention using two case studies. For both case studies, LLM-Geo returned accurate results, including aggregated numbers, graphs, and maps, significantly reducing manual operation time. Although still lacking several important modules such as logging and code testing, LLM-Geo demonstrates a potential path towards 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：大型语言模型（LLMs，如ChatGPT）展现出对人类自然语言的深刻理解，并已在推理、创意写作、代码生成、翻译和信息检索等多个领域得到探索和应用。通过将LLM作为推理核心，我们提出自主地理信息系统（Autonomous GIS），这是一种AI驱动的地理信息系统，利用LLM在自然语言理解、推理和编码方面的通用能力，自动执行空间数据收集、分析和可视化，以解决空间问题。我们设想，自主地理信息系统需要实现五个自主目标，包括自我生成、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环境中开发了一个名为LLM-Geo的原型系统，通过两个案例研究展示了自主地理信息系统的形态及其如何无需人工干预即可交付预期结果。在两个案例中，LLM-Geo均返回了准确结果，包括汇总数据、图表和地图，显著减少了手动操作时间。尽管仍缺乏日志记录和代码测试等关键模块，LLM-Geo展示了一条通往下一代AI驱动地理信息系统的潜在路径。我们呼吁地理信息科学界投入更多精力研发自主地理信息系统，使空间分析更简便、快速，并惠及更广泛的用户群体。