Hierarchical Extraction of Skeleton Structures from Discrete Buildings

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.

KEYWORDS:

• Building generalization
• hierarchical skeleton structures
• centrality analysis
• stroke
• network theory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Deutsche Forschungsgemeinschaft [grant number DFG, BU 2605/5-1].

Notes on contributors

Xiao Wang

Xiao Wang has been a PhD student at Dresden University of Technology since 2016. His research fields include automated map generalization, multiple representation, spatial data matching and GIS.