TY - JOUR
T1 - MasterplanGAN
T2 - Facilitating the smart rendering of urban master plans via generative adversarial networks
AU - Ye, Xinyue
AU - Du, Jiaxin
AU - Ye, Yu
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by National Natural Science Foundation of China (52078343), Natural Science Foundation of Shanghai (20ZR1462200) and Open Grant from Key Laboratory of Ecology and Energy-saving Study of Dense Habitat (Tongji University), Ministry of Education (2020010102).
Publisher Copyright:
© The Author(s) 2021.
PY - 2022/3
Y1 - 2022/3
N2 - This study proposes a prototype for the smart rendering of urban master plans via artificial intelligence algorithms, a process which is time-consuming and relies on professionals’ experience. With the help of crowdsourced data and generative adversarial networks (GAN), a generation model was trained to provide colorful rendering of master plans similar to those produced by experienced urban designers. Approximately 5000 master plans from Pinterest were processed and CycleGAN was applied as the core algorithm to build this model, the so-called MasterplanGAN. Using the uncolored input design files in an AutoCAD format, the MasterplanGAN can provide master plan renderings within a few seconds. The validation of the generated results was achieved using quantitative and qualitative judgments. The achievements of this study contribute to the development of automatic generation of previously subjective and experience-oriented processes, which can serve as a useful tool for urban designers and planners to save time in real projects. It also contributes to push the methodological boundaries of urban design by addressing urban design requirements with new urban data and new techniques. This initial exploration indicates that a large but clear picture of computational urban design can be presented, integrating scientific thinking, design, and computer techniques.
AB - This study proposes a prototype for the smart rendering of urban master plans via artificial intelligence algorithms, a process which is time-consuming and relies on professionals’ experience. With the help of crowdsourced data and generative adversarial networks (GAN), a generation model was trained to provide colorful rendering of master plans similar to those produced by experienced urban designers. Approximately 5000 master plans from Pinterest were processed and CycleGAN was applied as the core algorithm to build this model, the so-called MasterplanGAN. Using the uncolored input design files in an AutoCAD format, the MasterplanGAN can provide master plan renderings within a few seconds. The validation of the generated results was achieved using quantitative and qualitative judgments. The achievements of this study contribute to the development of automatic generation of previously subjective and experience-oriented processes, which can serve as a useful tool for urban designers and planners to save time in real projects. It also contributes to push the methodological boundaries of urban design by addressing urban design requirements with new urban data and new techniques. This initial exploration indicates that a large but clear picture of computational urban design can be presented, integrating scientific thinking, design, and computer techniques.
KW - Deep learning
KW - MasterplanGAN
KW - crowdsourced data
KW - generative adversarial networks
KW - urban design
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U2 - 10.1177/23998083211023516
DO - 10.1177/23998083211023516
M3 - Article
AN - SCOPUS:85109015192
SN - 2399-8083
VL - 49
SP - 794
EP - 814
JO - Environment and Planning B: Urban Analytics and City Science
JF - Environment and Planning B: Urban Analytics and City Science
IS - 3
ER -