GANs for Urban Design

TL;DR

This paper explores using Generative Adversarial Networks to design urban blocks, enabling adaptable, style-translatable models that learn from existing city data without predefined parameters.

Contribution

It introduces a flexible GAN-based model for urban design that learns morphological features directly from city data, applicable across diverse urban contexts.

Findings

Model successfully adapts to different city morphologies

Enables style translation between cities

Provides both qualitative and quantitative evaluation

Abstract

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.