| Title |
Objective Typification of Building Exteriors Using Deep Learning |
| Authors |
안종규(An, Jong-Gyu) ; 조항만(Zo, Hangman) |
| DOI |
https://doi.org/10.5659/JAIK.2023.39.8.37 |
| Keywords |
CNN; k-means clustering; Typology |
| Abstract |
This study introduces an objective typification methodology that employs deep learning to analyze the exterior appearances of buildings. The
conventional approach to typification was reliant on subjective analysis and was limited in terms of the number of structures that could be
assessed. This study aimed to overcome these limitations by establishing an objective typification method using deep learning, focusing
specifically on public office buildings. The research process involved a comprehensive survey of domestic public office buildings to compile
an image dataset. Subsequently, a model was constructed utilizing Convolutional Neural Networks (CNN), a form of deep learning, to grasp
the distinctive features of building images. These features, extracted from the CNN model, were then organized into groups through k-means
clustering. The outcome of this clustering enabled the analysis of each cluster’s unique characteristics, facilitating the establishment of
typification criteria such as building height, fa?ade pattern, materials, protrusions, and roof structures. This methodology’s effectiveness was
validated through a comparative analysis with prior research. The results of this study offer potential applications in fundamental
investigations concerning the current state of public office buildings and in typification studies encompassing diverse architectural forms
beyond public office buildings. |