CHARACTERIZING RESIDENTIAL BUILDING PATTERNS IN HIGHDENSITY CITIES USING GRAPH CONVOLUTIONAL NEURAL NETWORKS

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

In urban morphology studies, accurately classify ingresidential building patterns is crucial for informed zoning and urban design guidelines. While machine learning, particularly neural networks, has been widely applied to urban form taxonomy, most studies focus on grid-like data from street-view images or satellite imagery. Our paper provides a novel framework for graph classification by extracting features of clustering buildings at different scales and training a spectral-based GCN model on graph-structured data. Furthermore, from the perspective of urban designers, we put forward corresponding design strategies for different building patterns through data visualization and scenario analysis. The findings indicate that GCNhas a good performance and generalization ability in identify ingresidential building patterns, and this framework can aid urban designers or planners in decision-making for diverse urban environments in Asia.

Identifier

85196739783 (Scopus)

ISBN

[9789887891826]

Publication Title

Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia

External Full Text Location

https://doi.org/10.52842/conf.caadria.2023.2.039

e-ISSN

27104265

ISSN

27104257

First Page

39

Last Page

48

Volume

2

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