A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing
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 façad...
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MDPI AG
2023
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ftdoajarticles:oai:doaj.org/article:f4f9c1787d9e4b43810c3db98f3d2e75 2023-05-15T15:33:30+02:00 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 Lei Ding 2023-01-01T00:00:00Z https://doi.org/10.3390/su15031816 https://doaj.org/article/f4f9c1787d9e4b43810c3db98f3d2e75 EN eng MDPI AG https://www.mdpi.com/2071-1050/15/3/1816 https://doaj.org/toc/2071-1050 doi:10.3390/su15031816 2071-1050 https://doaj.org/article/f4f9c1787d9e4b43810c3db98f3d2e75 Sustainability, Vol 15, Iss 1816, p 1816 (2023) deep learning generative adversarial network (GAN) façade generation Pix2Pix generator comparison Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 article 2023 ftdoajarticles https://doi.org/10.3390/su15031816 2023-02-12T01:25:42Z 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 Article in Journal/Newspaper Attu Directory of Open Access Journals: DOAJ Articles Sustainability 15 3 1816 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
deep learning generative adversarial network (GAN) façade generation Pix2Pix generator comparison Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
spellingShingle |
deep learning generative adversarial network (GAN) façade generation Pix2Pix generator comparison Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Da Wan Runqi Zhao Sheng Zhang Hui Liu Lian Guo Pengbo Li Lei Ding A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing |
topic_facet |
deep learning generative adversarial network (GAN) façade generation Pix2Pix generator comparison Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
description |
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 |
format |
Article in Journal/Newspaper |
author |
Da Wan Runqi Zhao Sheng Zhang Hui Liu Lian Guo Pengbo Li Lei Ding |
author_facet |
Da Wan Runqi Zhao Sheng Zhang Hui Liu Lian Guo Pengbo Li Lei Ding |
author_sort |
Da Wan |
title |
A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing |
title_short |
A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing |
title_full |
A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing |
title_fullStr |
A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing |
title_full_unstemmed |
A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing |
title_sort |
deep learning-based approach to generating comprehensive building façades for low-rise housing |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/su15031816 https://doaj.org/article/f4f9c1787d9e4b43810c3db98f3d2e75 |
genre |
Attu |
genre_facet |
Attu |
op_source |
Sustainability, Vol 15, Iss 1816, p 1816 (2023) |
op_relation |
https://www.mdpi.com/2071-1050/15/3/1816 https://doaj.org/toc/2071-1050 doi:10.3390/su15031816 2071-1050 https://doaj.org/article/f4f9c1787d9e4b43810c3db98f3d2e75 |
op_doi |
https://doi.org/10.3390/su15031816 |
container_title |
Sustainability |
container_volume |
15 |
container_issue |
3 |
container_start_page |
1816 |
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1766364057851396096 |