GeoAI: Where machine learning and big data converge in GIScience

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.

Original languageEnglish (US)
Pages (from-to)71-77
Number of pages7
JournalJournal of Spatial Information Science
StatePublished - 2020


  • Big data
  • CNN
  • Convergence research
  • Data-driven
  • Deep learning
  • GeoAI
  • Knowledge graph

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Computers in Earth Sciences


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