A building aggregation method based on deep clustering of graph vertices
Building element aggregation is pivotal for simplifying spatial structures in cartographic generalization.Conventional rule-based aggregation methods often cannot simultaneously consider the morphological and distributional characteristics of the features,because they are heavily influenced by preset algorithm parameters and lack flexibility in the cartographic generalizing process.To fill these limitations,this paper proposes a building aggregation model based on deep clustering of graph vertices.The model utilizes the Delaunay triangulation network to construct a representation graph model of building groups and com-bines an autoencoder and graph convolutional network to learn the subdivided triangles'geometric shapes and spatial distribu-tion features.A self-supervised learning approach is employed to cluster and classify the triangles into the categories of"retain"and"delete".Consequently,it aggregates buildings intelligently in an end-to-end manner without relying on predefined sam-ples.The experimental results demonstrate that the proposed method reduces reliance on preset aggregation parameters while simultaneously considering building elements'morphology and distribution features.The aggregation process exhibits a certain degree of flexibility,resulting in aggregated buildings better aligning with the requirements of map visualization.
万方数据
维普
