Hierarchical attributed graph-based generative façade parsing for high-rise residential buildings.

High-rise residential building façades (HRBFs), given their size and abundant façade information, pose a challenge for conventional parsing methods. This paper presents FaçadeGraph, an approach for parsing the information of HRBFs into hierarchical attributed graphs. The method decomposes HRBF information into five hierarchical layers: ternary, floor, unit, space, and component. The façade elements are identified as semantics information with geometric attributes. The topological relationships between the elements are classified into affiliation, connection, aggregation, and decoration. The efficacy of FaçadeGraph was evaluated through the analysis of 36 HRBFs in China. The result showed that FaçadeGraph is effective in transforming diverse façade designs into consolidated graphs for automated syntax analyses. The paper contributes to the knowledge body of façade design by serving as an analytical tool for design feature analysis and underlying the development of generative HRBF design. • An attributed graph-based façade parsing approach is proposed. • A hierarchical structure is used to indicate façade elements for semantic analysis. • Universally applicable rules are defined for façade representation. • The framework is validated by a dataset of façades on 36 high-rise buildings. [ABSTRACT FROM AUTHOR]