Advancing intelligent geography: Current status, innovations, and future prospects

Abstract

Geography is shifting from static description to a feedback-driven, adaptive discipline integrating sensing, prediction, comparison, and continuous self-improvement. This transformation underlies Intelligent Geography (IG), where artificial intelligence (AI), big data analytics, and high-performance computing (HPC) converge to enhance spatial understanding and guide intelligent decisions in complex systems. The discipline’s historical stages—descriptive, experimental, theoretical, quantitative, GIScience, and information geography—form the foundation for an overarching adaptive framework. In this framework, diverse geospatial data streams seamlessly feed real-time models whose predicted outputs are compared with observed conditions to iteratively refine predictions. A hallmark of IG is embedding domain theory into AI workflows, producing predictive models that self-adjust to new data or control system behavior. Applications such as smart traffic management, climate-responsive urban planning, and disaster-resilient digital twins illustrate the sensing–prediction–adaptation/learning cycle in practice for complex changing systems. We examine the enabling roles of HPC, deep learning, and geographic large models in implementing feedback loops, and address persistent challenges in data integration, interpretability, and governance. We conclude with a vision of IG as an evolving socio-technical ecosystem that through adaptation and self-learning turns spatial data into adaptive, actionable knowledge that assists in intelligent decision-making, whether it is for AI systems or human ones.

Keywords

Traditional geography / High-performance computing / Big data analytics / Artificial intelligence (AI) / Digital twin / Geospatial modelling / Intelligent geography

CRediT authorship contribution statement

Fenzhen Su: Writing – original draft, Funding acquisition, Conceptualization. Fengqin Yan: Writing – original draft, Visualization, Funding acquisition, Data curation. Wenzhou Wu: Writing – original draft, Data curation. Dongjie Fu: Writing – original draft, Data curation. Yinxia Cao: Writing – original draft. Vincent Lyne: Writing – review & editing. Michael Meadows: Writing – review & editing. Ling Yao: Writing – review & editing. Jianghao Wang: Writing – review & editing. Yuanyuan Huang: Writing – review & editing. Chong Huang: Writing – review & editing. Jun Qin: Writing – review & editing. Shifeng Fang: Writing – review & editing. An Zhang: Writing – review & editing.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB0740300), the National Natural Science Foundation of China (Grant No. 42476195) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2023060).

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