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 (AutoGIS) 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 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 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，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，我们提出了自主式GIS（AutoGIS），这是一种利用LLM在自然语言理解、推理和编码方面的通用能力，自动完成空间数据采集、分析和可视化以解决空间问题的人工智能驱动地理信息系统（GIS）。我们设想，自主式GIS需实现五个自主目标，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Python环境中的GPT-4 API开发了一个名为LLM-Geo的原型系统，并通过三个案例研究展示了自主式GIS的形态及其在无需人工干预下交付预期结果的能力。在所有案例中，LLM-Geo均能返回准确结果，包括汇总数据、图表和地图，显著减少了人工操作时间。尽管仍处于初期阶段且缺少日志记录和代码测试等重要模块，LLM-Geo已展示了迈向下一代人工智能驱动GIS的潜在路径。我们呼吁地理信息科学界投入更多精力研发自主式GIS，使空间分析更简便、高效，并惠及更广泛的用户群体。