Clustering and Morphological Analysis of Campus Context (2020)
article⁄Clustering and Morphological Analysis of Campus Context (2020)
abstract⁄‘Figureground’ is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figureground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learningdeep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and bigdata methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.
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| Year |
2020 |
| Authors |
Li, Peiwen; Zhu, Wenbo. |
| Issue |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers |
| Pages |
170-177. |
| Library link |
N/A |
| Entry filename |
clustering-morphological-analysis-campus-context |
