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.

Landform type recognition presents significant implications for understanding landform origins, evolutionary mechanisms, and morphological differences. Artificial intelligence (AI) techniques based on sample learning often lead to unsatisfactory outcomes due to the intricate genesis and regional heterogeneity of landforms. This study combines domain knowledge with a deep learning (DL) model to improve landform type recognition. Contour data serves as a valuable resource, offering rich morphological information across horizontal, vertical, local, and macro scales. Our approach incorporated morphological knowledge and proximity relationships derived from contours into a graph convolutional network using the DiffPool technique (GCN-DP). Guided by the First Law of Geography, contours within each landform unit were represented as graphs, incorporating morphological knowledge as node features. The GCN-DP model then employed convolution and pooling to extract hierarchical features from these graphs for landform type recognition. A performance evaluation demonstrated the effectiveness of our method with an F1-score of 87.40%, surpassing RF and GCN methods by 5.24–12.50%, respectively. Ablation experiments confirmed the usefulness of morphological knowledge. This study offers an efficient strategy for landform type recognition, improving the level of intelligent mining using contour data.

Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering.

Research has developed numerous algorithms to simplify building data. Each algorithm has strengths and weaknesses in addressing shape characteristics, but no single algorithm can appropriately simplify all buildings. This study proposes a hybrid approach that identifies the best simplified representation of a building among four existing algorithms. The proposed approach applies the four algorithms to generate simplification candidates. With a backpropagation neural network, an evaluator is built through supervised learning based on measurements describing the changes in position, size, orientation, and shape between the original building and the candidates of its simplified representations. The evaluator determines the most appropriate candidate. Experiments on buildings from residential and commercial areas in Shenzhen city show that the hybrid approach can combine the advantages of different algorithms. The percentages of unreasonable simplified buildings in the results obtained using the hybrid algorithm are 3.8% in the residential area and 0 in the commercial area, respectively, which are significantly lower than those in the results of standalone applications of the four algorithms. Furthermore, comparison with the simplification algorithm in the popular software, ArcGIS, confirms that our approach shows better results in terms of corner squaring and maintaining the regional characteristics of buildings.

The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.