GeoAI for Science and the Science of GeoAI

Authors

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

DOI:

https://doi.org/10.5311/JOSIS.2024.29.349

Keywords:

artificial intelligence, spatially explicit, AI for science, responsible AI, explainable AI, GeoAI, reproducibility, co-design, ethics, AI for good

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.

349

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Published

2024-09-20

Issue

Section

Invited Articles