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A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing

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  • Da Wan

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
    Department of Architecture, Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

  • Runqi Zhao

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China)

  • Sheng Zhang

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China)

  • Hui Liu

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China)

  • Lian Guo

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China)

  • Pengbo Li

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China)

  • Lei Ding

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China)

Abstract

In recent years, as machine learning has been widely studied in the field of architecture, scholars have demonstrated that computers can be used to learn the graphical features of building façade generation. However, existing deep learning in façade generation has yet to generate only a single façade, without comprehensive generation of five façades including the roof. Moreover, most of the existing literature has utilized the Pix2Pix algorithm for façade generation experiments, failing to attempt to replace the original generator in Pix2Pix with a different generator for experiments. This study addresses the above issues by collecting and filtering entries from the international Solar Decathlon (SD competition) to obtain a data set. Subsequently, a low-rise residential building façade generation model based on the Pix2Pix neural network was constructed for training and testing. At the same time, the original U-net generator in Pix2Pix was replaced with three different generators, U-net++, HRNet and AttU-net, for training and test results were obtained. The results were evaluated from both subjective and objective aspects and it was found that the AttU-net generative network showed the best comprehensive generation performance for such façades. HRNet is acceptable if there is a need for fast training and generation

Suggested Citation

  • Da Wan & Runqi Zhao & Sheng Zhang & Hui Liu & Lian Guo & Pengbo Li & Lei Ding, 2023. "A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1816-:d:1039474
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    References listed on IDEAS

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    1. Da Wan & Xiaoyu Zhao & Wanmei Lu & Pengbo Li & Xinyu Shi & Hiroatsu Fukuda, 2022. "A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
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    Cited by:

    1. Xiaoni Gao & Xiangmin Guo & Tiantian Lo, 2023. "M-StruGAN: An Automatic 2D-Plan Generation System under Mixed Structural Constraints for Homestays," Sustainability, MDPI, vol. 15(9), pages 1-19, April.

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