Imaging Place Using Generative Adversarial Networks (GAN Loci)

Steinfeld, Kyle

doi:10.1002/9781119815075.ch42

Cited by 2 publications

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“…At the urban level, the initial applications of deep learning mainly focus on the generation of urban streetscape maps [15] and quick previews of urban block renovations [17]. Subsequent related research gradually emerges in the generation of floor plans at different scales.…”

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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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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“…17.See, for example, the work of Schmitt and Weiß in furniture design, 60 Tyler Kvochick’s 2017 “Generating the Generator” 61 which was the first attempt to generate architectural plans using GANs, and Kyle Steinfeld’s 2018 “Almost Home” 62 which was the first attempt at generating 3days architectural massings using GANs.…”

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confidence: 99%

Clever little tricks: A socio-technical history of text-to-image generative models

Steinfeld

2023

International Journal of Architectural Computing

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The emergence of text-to-image generative models (e.g., Midjourney, DALL-E 2, Stable Diffusion) in the summer of 2022 impacted architectural visual culture suddenly, severely, and seemingly out of nowhere. To contextualize this phenomenon, this text offers a socio-technical history of text-to-image generative systems. Three moments in time, or “scenes,” are presented here: the first at the advent of AI in the middle of the last century; the second at the “reawakening” of a specific approach to machine learning at the turn of this century; the third that documents a rapid sequence of innovations, dubbed “clever little tricks,” that occurred across just 18 months. This final scene is the crux, and represents the first formal documentation of the recent history of a specific set of informal innovations. These innovations were produced by non-affiliated researchers and communities of creative contributors, and directly led to the technologies that so compellingly captured the architectural imagination in the summer of 2022. Across these scenes, we examine the technologies, application domains, infrastructures, social contexts, and practices that drive technical research and shape creative practice in this space.

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Imaging Place Using Generative Adversarial Networks (GAN Loci)

Cited by 2 publications

References 1 publication

Nolli Map: Interpretation of Urban Morphology Based on Machine Learning

Nolli Map: Interpretation of Urban Morphology Based on Machine Learning

Clever little tricks: A socio-technical history of text-to-image generative models

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