基于图顶点深度聚类的建筑物合并方法

A building aggregation method based on deep clustering of graph vertices

建筑物要素合并是大比例尺地图缩编过程中实现空间结构简化的重要手段。基于综合规则的合并方法难以同时顾及要素形态、分布等诸多特征,受预设算法参数影响大,综合过程缺乏灵活性。针对这一问题,本文提出了一种基于图顶点深度聚类网络的建筑物合并模型,利用Delaunay三角网构建建筑物群组表征图模型,结合自编码器与图卷积网络学习剖分三角形的几何形态、空间分布特征,采用自监督学习方式实现三角形的聚类与分类(保留、删除),最终在不依赖样本条件下实现建筑物要素端到端智能化合并。试验表明,该方法对预设合并参数依赖低,能同时顾及建筑物要素的形态与分布特征。合并过程具有一定灵活性,合并结果能较好满足地图可视化要求。

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 combines an autoencoder and graph convolutional network to learn the subdivided triangles’geometric shapes and spatial distribution 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 samples.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.