In recent decades, the infusion of statistics and dynamic equations into geography has shifted the discipline from a descriptive endeavor to a modern science that builds predictive models through hypothesis testing. This progress, however, prompted doubts about the representativeness of sampling, the validity of discarded outliers, and the uniqueness of assumed distributions, parameters, and equations.
Modern sensing technologies have spawned big data, which is now driving AI to describe, analyze, and simulate geographic systems—forming a new research paradigm that is non-hypothesis-testing and less demanding of complete mechanistic understanding.
In this paper entitled
Advancing Intelligent Geography: Current status, innovations, and future prospects, Prof. Fenzhen Su
et al. examines the evolution of geography from a methodological standpoint, proposes the research direction of Intelligent Geography, and analyzes its current state of research and related controversies.
Highlights:
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.
Core content:
- Development of geography
The evolution of geography is deeply intertwined with the progress of human civilization, unfolding through several overlapping key stages: descriptive geography, experimental geography, theoretical geography, quantitative geography, geographic information science, and information geography.
- Evolution of IG elements
The modern advancement of information technology has significantly transformed geography through three key developments: the proliferation of big data in the Internet of Everything (IoE), the exponential growth in computing power, and the rapid evolution of AI. These elements form the technological backbone of IG, allowing for real-time analysis, predictive modelling, and enhanced decision-making capabilities.
- Framework for IG
Building on the great development of human information technology (big data, computing power, and artificial intelligence), and with the advancements in big data, geographical large models, and geospatial digital twins, geography is moving towards a new era of IG. A hallmark of IG is embedding domain theory into AI workflows, producing predictive models that self-adjust to new data or control system behavior.
- Key issues
The core challenge for IG is addressing the Geospatial AI trilemma, which involves overcoming data quality flaws, model interpretability issues (“black-box” problem), and ethical dilemmas like privacy and fairness while leveraging complex models. This necessitates deep interdisciplinary integration, a re-evaluation of its ontological foundations, and robust safety-ethical governance to ensure its decision-support is both intelligent and trustworthy.
Outlook: Future development of Intelligent Geographics (IG) will focus on establishing a framework for deep integration and efficient analysis of multi-source spatio-temporal data. Key directions include enhancing AI model interpretability, building robust models that balance accuracy and efficiency, and developing user-friendly interfaces like natural language interaction. The ultimate goal is to form an adaptive, intelligent paradigm capable of supporting high-resolution geographic modeling and scientific decision-making, thereby providing reliable spatial intelligence for sustainable development.
DOI:10.1016/j.geosus.2025.100375