  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.
