| id |
acadia20_170 |
| authors |
Li, Peiwen; Zhu, Wenbo |
| year |
2020 |
| title |
Clustering and Morphological Analysis of Campus Context |
| source |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 170-177. |
| doi |
https://doi.org/10.52842/conf.acadia.2020.2.170
|
| summary |
“Figure-ground” 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 figure-ground 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 learning/deep 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 big-data 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. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (5,003,480 bytes) |
| references |
Content-type: text/plain
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Dong, J., L. Li and D. Han (2019)
 New Quantitative Approach for the Morphological Similarity Analysis of Urban Fabrics Based on a Convolutional Autoencoder
, IEEE Access 7: 138162–74
|
|
|
|
|
Li, X., S. Cheng, K. Li, and C. Chen (2017)
Data Analysis of Urban Fabric—A Case Study of Hankou Yanjiang Area
, Architectural Journal (S1): 7–13
|
|
|
|
|
Meng, Zhang (2018)
Research on Outer Space Morphology of Primary and Secondary Schools Based on Space Syntax
, Master's thesis, Southeast University
|
|
|
|
|
Rhee, Jinmo (2019)
Context-rich Urban Analysis Using Machine Learning—A Case Study in Pittsburgh, PA
, Architecture in the Age of the 4th Industrial Revolution—Proceedings of the 37th eCAADe and 23rd SIGraDi Conference, edited by J.P. Sousa, J. P. Xavier, and G. Castro Henriques, 343–52. Portugal. CUMINCAD
|
|
|
|
|
van der Maaten, L. J. P. and G.E. Hinton (2008)
Visualizing High-Dimensional Data Using t-SNE
, Journal of Machine Learning Research 9 (Nov): 2579-2605
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| last changed |
2023/10/22 12:06 |
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