A Preliminary Study on the Formation of the General Layouts on the Northern Neighborhood Community Based on GauGAN Diversity Output Generator

Pan, Yuzhe; Qian, Jin; Hu, Yafei

doi:10.1007/978-981-33-4400-6_17

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…In terms of research on diversity generation of layouts. Pan [6] obtained the results of diverse generation by using GauGAN model to generate the layout of the neighborhood community in north of China. This study showed us the possibility of deep learning for diverse layout output, which affected the output results by changing the images of the input.…”

Section: Related Work In the Field Of Architectural Layoutmentioning

confidence: 99%

Exploration on Diversity Generation of Campus Layout Based on GAN

Liu¹,

Zhang²,

Deng³

2023

Computational Design and Robotic Fabrication

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Previous studies have shown that GAN has made some progress in the generation of campus layout plan, but the result is single output for single input condition. This paper hopes to make some attempts and explorations on the diversity generation of campus planning layout design by machine learning. Based on Pix2Pix model, this paper proposes a method to divide image channels so that both the campus function bubble diagram and the site boundary can both become the input conditions. There is a strong correspondence between the campus functional bubble diagram and the campus layout. The main idea of this study is to control the generated results by changing the input of the campus functional bubble diagram, so that we can have a diversity layout of campus according to the same site conditions. In the experiment, we train thirty samples of campus planning layout design, and finally evaluate the generated results in a qualitative and quantitative way, which proves that the generated results are relatively ideal. This research enables designers to participate in the process of machine learning generative design to control the generation results.

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Section: Related Work In the Field Of Architectural Layoutmentioning

confidence: 99%

Exploration on Diversity Generation of Campus Layout Based on GAN

Liu¹,

Zhang²,

Deng³

2023

Computational Design and Robotic Fabrication

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“…Pasquero and Poletto [11] used cycleGAN to explore "non-human" urban forms by transferring biomorphic styles to the urban fabric. Pan et al [10] trained a large sample of northern neighborhoods to generate diverse layouts within a plot [10]. Fedorova [2] used the model trained by five existing urban environments to observe the possibility of style transfer between different cities, and presented quantitative and qualitative evaluations of the results (Table 2).…”

Section: Applications Of Deep Learning In Urban Designmentioning

confidence: 99%

Nolli Map: Interpretation of Urban Morphology Based on Machine Learning

Zhang

Lin

2023

Computational Design and Robotic Fabrication

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Nolli map is the earliest diagram tool to simplify and quantify urban form, which most intuitively reflects the spatial layout of tangible elements in the city. The urban morphology contains its inherent evolutionary laws. Exploring the inner rules of cities is helpful for people to conduct urban research and design. Unlike the traditional research methods of urban morphology, the neural network algorithm provides us with new ideas for understanding urban morphology. In this experiment, we label 136 European cities samples in the rules of Nolli map as a training set for machine learning. We use Generative Adversarial Networks (GAN) for multiple mapping experiments. The generated images present recognizable and plausible images of the urban fabric. The results show that the machine can learn the inherent laws of complex urban fabrics, which expands a new applied method for the study of urban morphology.

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“…GauGAN is an image translation algorithm published by Nvidia Lab in 2019 to achieve multi-modal synthesis (Park et al, 2019). Compared to the Pix2pix model, the GauGAN model is much more precise, allowing the generated bitmap to be more easily converted into a vector graph (Pan et al, 2020). The resulting GAN bitmaps are converted back to a 3D model, as shown in Figure 3.…”

Section: Gan Model Training and Vectorized Model Generationmentioning

confidence: 99%

A Discussion on an Urban Layout Workflow Utilizing Generative Adversarial Network (GAN) - With a focus on automatized labeling and dataset acquisition

Zhong¹,

Fricker²,

Yu³

et al. 2022

eCAADe Proceedings

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Deep Learning (DL) has recently gained widespread attention in the automation of urban layout processes. This study proposes a rule-based and Generative Adversarial Network (GAN) workflow to automatically select and label urban datasets to train customized GAN models for the generation of urban layout proposals. The developed workflow automatically collects and labels urban typology samples from open-source maps. Furthermore, it controls the results of the GAN process with labels and provides real-time urban layout suggestions based on a co-design process. The conducted case study shows that the average value of the GAN results, trained from an automatically generated dataset, meets the site's requirements. The developed co-design strategy allows the architect to control the GAN process and perform iterations on urban layouts. The research addresses the research gap in GAN applications in the field of urban design and planning. Many studies have demonstrated that training the (GAN) model by labeling enables machines to learn urban morphological features and urban layout logic. However, two research gaps remain: (1) The manual filtering of GAN urban sample datasets to fit site-specific design requirements is very time-consuming. (2) Without a suitable data labeling method, it is difficult to manage the GAN process in such a manner to facilitate the meeting of overriding design requirements.

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A Preliminary Study on the Formation of the General Layouts on the Northern Neighborhood Community Based on GauGAN Diversity Output Generator

Cited by 7 publications

References 3 publications

Exploration on Diversity Generation of Campus Layout Based on GAN

Exploration on Diversity Generation of Campus Layout Based on GAN

Nolli Map: Interpretation of Urban Morphology Based on Machine Learning

A Discussion on an Urban Layout Workflow Utilizing Generative Adversarial Network (GAN) - With a focus on automatized labeling and dataset acquisition

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