This paper proposes the production of synthetic images of cities using generative adversarial networks (or GANs) represents the first computational approach to documenting the Genius Loci of a city, which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN and Pix2Pix) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images.

This post on the Towards Data Science blog concisely summarizes the GAN Loci project - a project which applies techniques in machine learning in order to produce synthetic images intended to capture the predominant visual properties of urban places.

This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto.

This workshop offered at the Design Modeling Symposium in Berlin presents tools and techniques for the application of Machine Learning (ML) to Generative Architectural Design (GAD).

The nature of design tools is related to the social relationships they serve. This paper speculates on the emergence of a new professional configuration - the synthesis of architect and engineer - and on the nature of new computational tools and methods that will be required to support such a reconfiguration.