Abstract: As a fundamental aspect of map generalization, building selection requires thorough consideration of various contextual factors such as size, orientation, shape, density, and more. However, many existing methods have only focused on single or a few factors, often relying on manual selection rules and parameters, which limits their practicality. In this study, we propose a data-driven building selection method using the graph convolutional network(GCN). Our method organizes buildings into a graph using Delaunay triangulation, with nodes representing building centers and edges denoting adjacent relationships between buildings. The size, orientation, shape, and density of each building are computed as the descriptive features for the associated nodes. Further, a GCN is constructed by stacking multiple graph Fourier convolutions and trained with semi-supervised learning to enable it to decide whether a building is selected or not. Experiments show that our method can effectively learn selection knowledge with few labels and perform well in maintaining the original spatial distribution densities and selecting important individual objects. It overcomes the difficulties in rule definition and parameter setting of traditional methods and does not rely on a large number of manual labels, which provides a promising solution for intelligent generalization.

Key words: map generalization, building selection, graph convolutional network, semi-supervised learning