Explainable GeoAI: Can saliency maps help interpret artificial
intelligence's learning process? An empirical study on natural feature
detection

Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection

16 March 2023

Chia-Yu Hsu

Papers citing "Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection"

9 / 9 papers shown

Title
Geospatial Artificial Intelligence for Satellite-based Flood Extent Mapping: Concepts, Advances, and Future Perspectives Hyunho Lee Wenwen Li AI4CE 43 0 0 03 Apr 2025
Interpretable Cross-Sphere Multiscale Deep Learning Predicts ENSO Skilfully Beyond 2 Years Rixu Hao Yuxin Zhao Shaoqing Zhang Guihua Wang Xiong Deng AI4Cl AI4CE 39 0 0 27 Mar 2025
A comprehensive GeoAI review: Progress, Challenges and Outlooks Anasse Boutayeb Iyad Lahsen-cherif Ahmed El Khadimi 89 0 0 16 Dec 2024
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery Hyunho Lee Wenwen Li AI4CE 25 9 0 29 Apr 2024
GeoAI Reproducibility and Replicability: a computational and spatial perspective Wenwen Li Chia-Yu Hsu Sizhe Wang Peter Kedron AI4CE 28 6 0 15 Apr 2024
Enhancing Explainability in Mobility Data Science through a combination of methods Georgios Makridis Vasileios Koukos G. Fatouros D. Kyriazis 19 4 0 01 Dec 2023
Assessment of a new GeoAI foundation model for flood inundation mapping Wenwen Li Hyunho Lee Sizhe Wang Chia-Yu Hsu S. Arundel AI4CE 15 17 0 25 Sep 2023
Towards eXplainable AI for Mobility Data Science Anahid N. Jalali Anita Graser Clemens Heistracher 14 2 0 17 Jul 2023
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US Timnit Gebru J. Krause Yilun Wang Duyun Chen Jia Deng Erez Aiden Lieberman Li Fei-Fei HAI 93 414 0 22 Feb 2017