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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Published in:Sustainability
Main Authors: Da Wan, Runqi Zhao, Sheng Zhang, Hui Liu, Lian Guo, Pengbo Li, Lei Ding
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
Subjects:
Online Access:https://doi.org/10.3390/su15031816
https://doaj.org/article/f4f9c1787d9e4b43810c3db98f3d2e75
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spelling 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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