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_id	caadria2022_45
id	caadria2022_45
authors	Boim, Anna, Dortheimer, Jonathan and Sprecher, Aaron
year	2022
title	A Machine-Learning Approach to Urban Design Interventions In Non-Planned Settlements
doi	https://doi.org/10.52842/conf.caadria.2022.1.223
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 223-232
summary	This study presents generative adversarial networks (GANs), a machine-learning technique that can be used as an urban design tool capable of learning and reproducing complex patterns that express the unique spatial qualities of non-planned settlements. We report preliminary experimental results of training and testing GAN models on different datasets of urban patterns. The results reveal that machine learning models can generate development alternatives with high morphological resemblance to the original urban fabric based on the suggested training process. This study contributes a methodological framework that has the potential to generate development alternatives sensitive to the local practices, thereby promoting preservation of traditional knowledge and cultural sustainability.
keywords	Non-planned settlements, Cultural Sustainability, Machine Learning, Generative Adversarial Networks, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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