GAN Loci (2019)
article⁄GAN Loci (2019)
abstract⁄This project applies techniques in machine learning, specifically generative adversarial networks or GANs, to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city NorbergSchulz, 1980, 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 Karras et al., 2018 and Pix2Pix Isola et al., 2016 that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computerassisted design CAD. In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user Aish, Glynn, Sheil 2017, in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
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| Year |
2019 |
| Authors |
Steinfeld, Kyle. |
| Issue |
ACADIA 19:UBIQUITY AND AUTONOMY |
| Pages |
392-403 |
| Library link |
N/A |
| Entry filename |
gan-loci |
