  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.
