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GeoAI for Science and the Science of GeoAI

  • Wenwen Li
  • , Samantha T. Arundel
  • , Song Gao
  • , Michael F. Goodchild
  • , Yingjie Hu
  • , Shaowen Wang
  • , Alexander Zipf
  • Arizona State University
  • United States Geological Survey
  • University of Wisconsin-Madison
  • University of California at Santa Barbara
  • University of Illinois at Urbana-Champaign
  • Heidelberg University 

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

This paper reviews trends in GeoAI research and discusses cutting-edge advances in GeoAI and its roles in accelerating environmental and social sciences. It addresses ongoing attempts to improve the predictability of GeoAI models and recent research aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findings. The paper also provides reflections on the importance of defining the “science” of GeoAI in terms of its fundamental principles, theories, and methods to ensure scientific rigor, social responsibility, and lasting impacts.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalJournal of Spatial Information Science
Issue number29
DOIs
StatePublished - 2024

Keywords

  • AI for Good
  • AI for science
  • GeoAI
  • artificial Intelligence
  • co-design
  • ethics
  • explainable AI
  • reproducibility
  • responsible AI
  • spatially explicit

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