GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

Li, Zhenlong; Ning, Huan; Gao, Song; Janowicz, Krzysztof; Li, Wenwen; Arundel, Samantha T.; Yang, Chaowei; Bhaduri, Budhendra; Wang, Shaowen; Zhu, A-Xing; Gahegan, Mark; Shekhar, Shashi; Ye, Xinyue; McKenzie, Grant; Cervone, Guido; Hodgson, Michael E.

doi:10.1080/19475683.2025.2552161

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

Journal Article · Fri Sep 12 00:00:00 EDT 2025 · Annals of GIS

DOI:https://doi.org/10.1080/19475683.2025.2552161· OSTI ID:3002131

Li, Zhenlong ^[1]; Ning, Huan ^[1]; ^[2]; Janowicz, Krzysztof ^[3]; Li, Wenwen ^[4]; Arundel, Samantha T. ^[5]; Yang, Chaowei ^[6]; ^[7]; Wang, Shaowen ^[8]; Zhu, A-Xing ^[2]; Gahegan, Mark ^[9]; Shekhar, Shashi ^[10]; Ye, Xinyue ^[11]; McKenzie, Grant ^[12]; Cervone, Guido ^[1]; Hodgson, Michael E. ^[13]

Pennsylvania State Univ., University Park, PA (United States)
Univ. of Wisconsin, Madison, WI (United States)
Univ. of Vienna (Austria)
Arizona State Univ., Tempe, AZ (United States)
US Geological Survey, Reston, VA (United States)
George Mason Univ., Fairfax, VA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States)
Univ. of Auckland (New Zealand)
Univ. of Minnesota, Minneapolis, MN (United States)
Univ. of Alabama, Tuscaloosa, AL (United States)
McGill Univ., Montreal, QC (Canada)
Univ. of South Carolina, Columbia, SC (United States)

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 3002131

Journal Information:: Annals of GIS, Journal Name: Annals of GIS Journal Issue: 4 Vol. 31; ISSN 1947-5683; ISSN 1947-5691

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

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