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.

本研究提出了通过人工智能算法智能渲染城市总体规划的原型，该过程耗时且依赖于专业人士的经验。在众包数据和生成对抗网络 (GAN) 的帮助下，训练生成模型以提供类似于经验丰富的城市设计师制作的总体规划的彩色渲染。处理了来自 Pinterest 的大约 5000 个总体规划，并应用 CycleGAN 作为核心算法来构建该模型，即所谓的 MasterplanGAN。使用 AutoCAD 格式的无色输入设计文件，MasterplanGAN 可以在几秒钟内提供总体规划效果图。生成结果的验证是使用定量和定性判断来实现的。这项研究的成果有助于自动生成以前主观和面向体验的过程，这可以作为城市设计师和规划者在实际项目中节省时间的有用工具。它还通过使用新的城市数据和新技术来满足城市设计要求，从而推动城市设计的方法论界限。这一初步探索表明，可以呈现出一幅大而清晰的计算城市设计图景，融合了科学思维、设计和计算机技术。它还通过使用新的城市数据和新技术来满足城市设计要求，从而推动城市设计的方法论界限。这一初步探索表明，可以呈现出一幅大而清晰的计算城市设计图景，融合了科学思维、设计和计算机技术。它还通过使用新的城市数据和新技术来满足城市设计要求，从而推动城市设计的方法论界限。这一初步探索表明，可以呈现出一幅大而清晰的计算城市设计图景，融合了科学思维、设计和计算机技术。