| id |
caadria2021_053 |
| authors |
Rhee, Jinmo and Veloso, Pedro |
| year |
2021 |
| title |
Generative Design of Urban Fabrics Using Deep Learning |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 31-40 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.031
|
| summary |
This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric. |
| keywords |
Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (14,139,233 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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