| id |
ecaade2024_161 |
| authors |
Wang, Yuyang; Li, Xingwu |
| year |
2024 |
| title |
The AI’s Cognition of the Cultural Characteristics Underlying Chinese Courtyard Dwelling: A visual perception-based approach for Siheyuan plan classification using machine learning and convolutional neural networks |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.643
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 643–652 |
| summary |
Siheyuan is a Chinese courtyard dwelling type in which the traditional culture is embedded. From the viewpoint of a spatially determinist understanding of culture, this study identifies the morphological characteristics of Siheyuan in a plan view constrained by Chinese culture. Based on the Visibility Graph Analysis (VGA) of the Siheyuan layouts, this study considers people’s visual perception in Siheyuan courtyards to classify the variants by using machine learning and computer vision techniques. According to the results of a clustering analysis of the feature vectors extracted from VGA maps, the spatial characteristics reflecting cultural connotation in Siheyuan design are captured. The findings could be used to support designing new Siheyuan-style dwellings and Siheyuan heritage preservation. |
| keywords |
Digital Heritage, Siheyuan Classification, Convolutional Neural Network, Visibility Graph Analysis, Clustering Analysis |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (634,295 bytes) |
| references |
Content-type: text/html
Access Temporarily Restricted
Access Temporarily Restricted
Too many requests detected. Please wait 86399 seconds or verify that you are a human.
If you are a human user and need immediate access, you can click the button below to continue:
If you continue to experience issues, please open a ticket at
papers.cumincad.org/helpdesk
|
| last changed |
2024/11/17 22:05 |
|
