Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. For example, in the task of facility siting, the Buffer tool is usually first used to locate areas close or away from some specific entities; then, the Intersect or Erase tool is used to select candidate areas satisfied multiple requirements. Though professionals can easily understand and solve these geospatial tasks by sequentially utilizing relevant tools, it is difficult for non-professionals to handle these problems. Recently, Generative Pre-trained Transformer (e.g., ChatGPT) presents strong performance in semantic understanding and reasoning. Especially, AutoGPT can further extend the capabilities of large language models (LLMs) by automatically reasoning and calling externally defined tools. Inspired by these studies, we attempt to lower the threshold of non-professional users to solve geospatial tasks by integrating the semantic understanding ability inherent in LLMs with mature tools within the GIS community. Specifically, we develop a new framework called GeoGPT that can conduct geospatial data collection, processing, and analysis in an autonomous manner with the instruction of only natural language. In other words, GeoGPT is used to understand the demands of non-professional users merely based on input natural language descriptions, and then think, plan, and execute defined GIS tools to output final effective results. Several cases including geospatial data crawling, spatial query, facility siting, and mapping validate the effectiveness of our framework. Though limited cases are presented in this paper, GeoGPT can be further extended to various tasks by equipping with more GIS tools, and we think the paradigm of "foundational plus professional" implied in GeoGPT provides an effective way to develop next-generation GIS in this era of large foundation models.

翻译：GIS决策者需要结合一系列空间算法和操作来解决地理空间任务。例如，在设施选址任务中，通常首先使用缓冲区工具定位靠近或远离特定实体的区域；随后通过交集或擦除工具选择满足多重条件的候选区域。虽然专业人员能够通过顺序调用相关工具轻松理解和解决这些地理空间任务，但非专业人员处理此类问题存在困难。近期，生成式预训练Transformer（如ChatGPT）在语义理解和推理方面展现出强大性能。特别是AutoGPT通过自动推理和调用外部定义工具，进一步拓展了大语言模型的能力。受这些研究启发，我们尝试通过整合LLM的语义理解能力与GIS领域成熟工具，降低非专业用户解决地理空间任务的准入门槛。具体而言，我们开发了名为GeoGPT的新框架，该框架仅需自然语言指令即可自主完成地理空间数据采集、处理与分析。换言之，GeoGPT仅基于输入的自然语言描述理解非专业用户需求，进而思考、规划并执行定义的GIS工具，最终输出有效结果。包括地理空间数据爬取、空间查询、设施选址和制图在内的多个案例验证了框架的有效性。尽管本文仅展示有限案例，但通过配备更多GIS工具，GeoGPT可扩展至各类任务，我们认为其蕴含的"基础+专业"范式为大型基础模型时代开发下一代GIS提供了有效路径。