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MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks

Author

Listed:
  • Xinyue Ye
  • Jiaxin Du

    (Texas A&M University, USA)

  • Yu Ye

    (12476Tongji University, China)

Abstract

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.

Suggested Citation

  • Xinyue Ye & Jiaxin Du & Yu Ye, 2022. "MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks," Environment and Planning B, , vol. 49(3), pages 794-814, March.
    • Handle: RePEc:sae:envirb:v:49:y:2022:i:3:p:794-814
    DOI: 10.1177/23998083211023516
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    References listed on IDEAS

    as
    1. Yu Ye & Wei Zeng & Qiaomu Shen & Xiaohu Zhang & Yi Lu, 2019. "The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images," Environment and Planning B, , vol. 46(8), pages 1439-1457, October.
    2. Reinhard Koenig & Yufan Miao & Anna Aichinger & Katja Knecht & Kateryna Konieva, 2020. "Integrating urban analysis, generative design, and evolutionary optimization for solving urban design problems," Environment and Planning B, , vol. 47(6), pages 997-1013, July.
    3. Ariel Noyman & Ronan Doorley & Zhekun Xiong & Luis Alonso & Arnaud Grignard & Kent Larson, 2019. "Reversed urbanism: Inferring urban performance through behavioral patterns in temporal telecom data," Environment and Planning B, , vol. 46(8), pages 1480-1498, October.
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