Development and diffusion of machine learning and big data tools provide a new tool for architects and urban planners that could be used as analytical or design instruments. The topic investigated in this paper is the application of Generative Adversarial Networks to the design of an urban block. The research presents a flexible model able to adapt to the morphological characteristics of a city. This method does not define explicitly any of the parameters of an urban block typical for a city, the algorithm learns them from the existing urban context. This approach has been applied to the cities with different morphology: Milan, Amsterdam, Tallinn, Turin, and Bengaluru in order to see the performance of the model and the possibility of style translation between different cities. The data are gathered from Openstreetmap and Open Data portals of the cities. This research presents the results of the experiments and their quantitative and qualitative evaluation.
翻译:机器学习和大数据工具的开发和传播为建筑师和城市规划者提供了一个新的工具,可以用作分析或设计工具,本文所调查的主题是将创用反versarial网络应用于设计一个城市区块,该研究提供了一个灵活的模型,能够适应城市的形态特征,该方法没有明确界定城市典型的城市区块的任何参数,算法从现有城市背景中学习这些参数,这一方法已应用于具有不同形态的城市:米兰、阿姆斯特丹、塔林、都灵和孟加拉鲁,以观察模型的性能和不同城市之间转换风格的可能性,数据来自城市的Openstreetmap和开放数据门户,该研究介绍了实验的结果及其定量和定性评估。