Autonomous GIS: the next-generation AI-powered GIS

Zhenlong Li, Huan Ning

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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, 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 returned accurate results, including aggregated numbers, graphs, and maps. 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 devote more efforts to the research and development of autonomous GIS, making spatial analysis easier, faster, and more accessible to a broader audience.

Original languageEnglish (US)
Pages (from-to)4668-4686
Number of pages19
JournalInternational Journal of Digital Earth
Volume16
Issue number2
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • General Earth and Planetary Sciences

Cite this