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 repro...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2024-09-20
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| In: |
Journal of spatial information science
Year: 2024, Jahrgang: 29, Pages: 1-17 |
| ISSN: | 1948-660X |
| DOI: | 10.5311/JOSIS.2024.29.349 |
| Online-Zugang: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.5311/JOSIS.2024.29.349 Verlag, kostenfrei, Volltext: https://josis.org/index.php/josis/article/view/349 
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| Verfasserangaben: | Wenwen Li, Samantha Arundel, Song Gao, Michael Goodchild, Yingjie Hu, Shaowen Wang, and Alexander Zipf |
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| 520 | |a 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. | ||
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