A Graph-based multi view clustering approach for building group patterns recognition

Ruolin Yang; Lin Yang; Zejun Zuo; Jing Wei

doi:10.1117/12.2683843

14 June 2023 A Graph-based multi view clustering approach for building group patterns recognition

Ruolin Yang, Lin Yang, Zejun Zuo, Jing Wei

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 1270819 (2023) https://doi.org/10.1117/12.2683843
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

Building pattern informs urban spatial structure understanding and modeling. However, previous studies showed limitations on the identification of building groups which has complex spatial distribution. Specifically, they usually use original spatial or non-spatial characteristics but omit certain complementary among multi-faceted features of buildings. In this paper, we give a novel method based on a multi-view clustering framework, which establishes a centroid distance view and a building attribute view to recognize building patterns accurately. The two views are based on both the spatial structure and non-spatial attributes. The similarity graphs on these views, which are obtained by the Gaussian kernel function, are executed graph diffusion and fusion process to obtain a unified graph. Then, the clustering results are obtained via the Normalized cut algorithm. We conduct experiments to recognize building patterns by four real-world community building footprint datasets of two cities in China: Wuhan and Chengdu. The experimental results show that our proposed method identifies building groups with grid, grid-like and unstructured patterns more effectively. Our method performs better in seven evaluation indexes compared with the baseline model.

Citation Download Citation

Ruolin Yang, Lin Yang, Zejun Zuo, and Jing Wei "A Graph-based multi view clustering approach for building group patterns recognition", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 1270819 (14 June 2023); https://doi.org/10.1117/12.2683843

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KEYWORDS

Matrices

Pattern recognition

Visualization

Reconstruction algorithms

Diffusion

Spatial analysis

Data modeling

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