The application of deep generative models in urban form generation based on topology: a review

Lin, Bo, Jabi, Wassim ORCID: https://orcid.org/0000-0002-2594-9568, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Lannon, Simon ORCID: https://orcid.org/0000-0003-4677-7184 2023. The application of deep generative models in urban form generation based on topology: a review. Architectural Science Review 10.1080/00038628.2023.2209550 Item availability restricted.

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Abstract

Integrating deep generative models into urban form generation is an innovative and promising approach to support the urban design process. However, most deep generative urban form models are based on image representations that do not explicitly consider topological relationships among urban form elements. Toward developing an urban form generation framework aided by deep generative models and considering topological information, this paper reviews urban form generation, deep generative models/deep graph generation, and the state of the art of deep generative models in architectural and urban form generation. Based on the literature review, a topology-based urban form generation framework aided by deep generative models is proposed. The hypotheses of street network generation by deep generative models for graph generation and plot/building configuration generation by deep generative models/space syntax and the feasibility of the proposed framework require validation in future research.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Architecture Computer Science & Informatics
Publisher:	Taylor and Francis Group
ISSN:	0003-8628
Date of First Compliant Deposit:	4 May 2023
Date of Acceptance:	27 April 2023
Last Modified:	11 Nov 2023 07:10
URI:	https://orca.cardiff.ac.uk/id/eprint/159200

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