TY - GEN
T1 - A MACHINE-LEARNING APPROACH TO URBAN DESIGN INTERVENTIONS IN NON-PLANNED SETTLEMENTS
AU - Boim, Anna
AU - Dortheimer, Jonathan
AU - Sprecher, Aaron
N1 - Publisher Copyright:
© 2022, The Association for Computer-Aided Architectural Design Research in Asia. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cultural Sustainability
KW - Generative Adversarial Networks
KW - Machine Learning
KW - Non-planned Settlements
KW - SDG 11
UR - http://www.scopus.com/inward/record.url?scp=85183103719&partnerID=8YFLogxK
U2 - 10.52842/conf.caadria.2022.1.223
DO - 10.52842/conf.caadria.2022.1.223
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AN - SCOPUS:85183103719
SN - 9789887891772
T3 - Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia
SP - 223
EP - 232
BT - POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1
A2 - van Ameijde, Jeroen
A2 - Gardner, Nicole
A2 - Hyun, Kyung Hoon
A2 - Luo, Dan
A2 - Sheth, Urvi
PB - The Association for Computer-Aided Architectural Design Research in Asia
T2 - 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022
Y2 - 9 April 2022 through 15 April 2022
ER -