GeoAI for Science and the Science of GeoAI
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.
Authors
- Yingjie Hu (ORCID: https://orcid.org/0000-0002-5515-4125)
- Michael Goodchild
- Shaowen Wang (ORCID: https://orcid.org/0000-0001-5848-590X)
- Samantha T. Arundel (ORCID: https://orcid.org/0000-0002-4863-0138)
- Alexander Zipf (ORCID: https://orcid.org/0000-0003-4916-9838)
- Wenwen Li (ORCID: https://orcid.org/0000-0003-2237-9499)
- Song Gao (ORCID: https://orcid.org/0000-0003-4359-6302)
Institutions
- Buffalo State University (US)
- United States Geological Survey (US)
- University of Wisconsin–Madison (US)
- University of California, Santa Barbara (US)
- University of Illinois Urbana-Champaign (US)
- Heidelberg University (DE)
- Pacific Island Ecosystems Research Center
- Arizona State University (US)
- University at Buffalo, State University of New York (US)
- Heidelberg University (US)
Publication Details
- Journal
- Journal of Spatial Information Science
- Published
- 2024-09-20
- DOI
- https://doi.org/10.5311/josis.2024.29.349
- Citations
- 25
- Primary Topic
- Advanced Computational Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 8.68
Funders
- National Science Foundation