Geospatial XAI: A Review

Abstract

Explainable Artificial Intelligence (XAI) has the potential to open up black-box machine learning models. XAI can be used to optimize machine learning models, to search for scientific findings, or to improve the understandability of the AI system for the end users. Geospatial XAI refers to AI systems that apply XAI techniques to geospatial data. Geospatial data are associated with geographical locations or areas and can be displayed on maps. This paper provides an overview of the state-of-the-art in the field of geospatial XAI. A structured literature review is used to present and discuss the findings on the main objectives, the implemented machine learning models, and the used XAI techniques. The results show that research has focused either on using XAI in geospatial use cases to improve model quality or on scientific discovery. Geospatial XAI has been used less for improving understandability for end users. The used techniques to communicate the AI analysis results or AI findings to users show that there is still a gap between the used XAI technique and the appropriate visualization method in the case of geospatial data.

Keywords

• explainable AI
• geospatial ML
• evaluation metrics
• visualization