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

Da Wan, Runqi Zhao, Sheng Zhang (), Hui Liu, Lian Guo, Pengbo Li and Lei Ding
Additional contact information
Da Wan: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
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

Sustainability, 2023, vol. 15, issue 3, 1-15

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

Keywords: deep learning; generative adversarial network (GAN); façade generation; Pix2Pix; generator comparison (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/3/1816/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/3/1816/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:3:p:1816-:d:1039474

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().