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

作者：Fenzhen Su, Fengqin Yan, Wenzhou Wu, Dongjie Fu, Yinxia Cao, Vincent Lyne, Michael E. Meadows, Ling Yao, Jianghao Wang, Yuanyuan Huang, Chong Huang, Jun Qin, Shifeng Fang, An Zhang · 发表于：Geography and sustainability · 年份：2025 · DOI：10.1016/j.geosus.2025.100375 · 被引用次数：7 · 研究领域：Geographic Information Systems Studies、Big Data Technologies and Applications、Remote-Sensing Image Classification

• This study traces geography’s evolution and introduces Intelligent Geography. • It compares Intelligent Geography and GeoAI to clarify key differences. • It explores big data, AI, HPC, and core tools like digital twins and deep learning. • Findings show great promise but raise concerns on security and model clarity. • Ethical AI and integration frameworks are vital to address these challenges. 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 cyc...