Explainable artificial intelligence approach for road network selection based on a neural additive model
Min Ji Yang, Xiao Xu, Taiyang Yang, Xiongfeng Yan · International Journal of Geographical Information Systems · 2025
Road network selection is critical in map generalization. While machine learning-based models improve selection performance, their increased complexity reduces explainability, which complicates the explicit description of the relationship between input road features and selection decisions. To address this issue, we propose an explainable artificial intelligence (XAI) approach for road network selection that treats road strokes as processing units and extracts the descriptive features for each stroke. A neural additive model (NAM) that consists of several independent feature networks was used to analyze descriptive features to determine whether each stroke should be retained. The explainability of the XAI approach was driven by the learning of linearly combined feature networks in the NAM, which clearly differentiates the contribution of each stroke’s features to the final selection results. To balance explainability and performance, knowledge distillation was employed to transfer knowledge from a teacher model to the NAM student model. Experiments on datasets showed that the XAI approach achieved over 87% consistency with manual selection. Notably, it provided a mechanism for both global and local explainability analyses, thereby improving our understanding of why certain strokes are retained or deleted. These insights into the model’s decision-making process help advance the automation of map generalization.