Hierarchical Extraction of Skeleton Structures from Discrete Buildings
Hierarchical Extraction of Skeleton Structures from Discrete Buildings
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DOI：
10.1080/00087041.2020.1852512
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Xiao Wang;D. Burghardt
中科院分区：
文献类型：
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作者：
Xiao Wang;D. Burghardt
摘要地图综合是一个按层次重新组织要素的过程，通过这个过程可以在不同的尺度上转换原始数据集的全局形状。我们提出了一种基于笔划和中心度的方法来分层提取建筑物的骨架结构，旨在支持泛化。首先，从细化的邻近图网络中生成笔划。接着，将笔画视为对偶图，计算每个笔画的三个中心性指数，从而创建一个综合因子来衡量笔画的重要性水平。最后，通过不同的选择比，提取基于笔划重要性水平的层次骨架结构。通过对建筑物进行分类，根据其特点选择不同的综合算子。实验结果表明，所提取的层次骨架结构可以代表整个区域的全局形状。通过这种支持，原始建筑的全球和地方模式都可以得到保护。
ABSTRACT Map generalization is a process of hierarchically reorganizing features whereby the global shape of the original datasets can be transferred in different scales. We propose a stroke and centrality-based method to hierarchically extract the skeleton structures from buildings aiming to support generalization. Firstly, the strokes are generated from refined proximity graph network. Next, by regarding the strokes as dual graph, three centrality indices are calculated for each stroke whereby an integrated factor is created to measure the importance level of the strokes. Finally, the hierarchical skeleton structures are extracted based on the stroke importance levels through different selection ratios. By classifying the buildings into different categories, different generalization operators are selected considering their characteristics. The experimental results demonstrate that the extracted hierarchical skeleton structures can represent the global shape of the entire region. Through this support, the global and local patterns of the original buildings can be both preserved.