GeoAI for science and the science of GeoAI
Journal of Spatial Information Science
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Abstract
This paper reviews trends in GeoAI research and discusses cutting-edge ad- vances in GeoAI and its roles in accelerating environmental and social sciences. It ad- dresses ongoing attempts to improve the predictability of GeoAI models and recent re- search 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.
Suggested Citation
Li, W., Arundel, S., Gao, S., Goodchild, M.F., Hu, Y., Wang, S., and Zipf, A., 2024, GeoAI for science and the science of GeoAI: Journal of Spatial Information Science, no. 29, 17 p., https://doi.org/10.5311/JOSIS.2024.29.349.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | GeoAI for science and the science of GeoAI |
| Series title | Journal of Spatial Information Science |
| DOI | 10.5311/JOSIS.2024.29.349 |
| Issue | 29 |
| Publication Date | September 20, 2024 |
| Year Published | 2024 |
| Language | English |
| Publisher | University of Maine |
| Contributing office(s) | Center for Geospatial Information Science (CEGIS) |
| Description | 17 p. |