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Lookup NU author(s): Dr Jin XingORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Although eXplainable Artificial Intelligence (XAI) has huge potential to glassbox deep learning models, there are challenges in applying it in the domain of Geospatial Artificial Intelligence (GeoAI), specifically Deep Neural Networks. We summarize these challenges, which include the difficulty of selecting reference data/models, the shortcomings of gradients as explanation, the challenges of accommodating geographic scale, the limitations of knowledge scope in the explanation process of GeoAI, the lack of acknowledging non-technical aspects in XAI, the incompatibility of geography in XAI visualization, as well as underlying geographic data analytical processes that are not amenable to XAI.
Author(s): Xing J, Sieber R
Editor(s): Mai, G; Cai, L;
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 1st International Workshop on Methods, Models, and Resources for Geospatial Knowledge Graphs and GeoA
Year of Conference: 2021
Print publication date: 27/09/2021
Online publication date: 27/09/2021
Acceptance date: 31/08/2021
Date deposited: 14/12/2021
URL: https://ling-cai.github.io/GIScience-GeoKG/
