지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.8
- 수록면
- 37 - 48 (12page)
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초록· 키워드
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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.
목차
Abstract
1. 서론
2. 문헌고찰
3. 딥러닝 기반 외관 유형화 방법론
4. 분석 결과
5. 결과 해석 및 논의
6. 결론 및 의의
REFERENCES
참고문헌 (32)
참고문헌 신청
Ahn, E. (2021). An analysis on the evolution of Korean apartment unit plans using deep learning. Journal of the Architectural Institute of Korea, 37(10), 13-22.
Ahn, J. K. (2014). Study on images of a city and surface design of public office buildings. Journal of Cultural Product & Design, 37(0), 81-90.
Archisoup (2017). Understanding building yypology. Retrieved April 2, 2023 from https://www.archisoup.com/understanding-building-typology
Arthur, D., & Vassilvitskii, S. (2007). K-means++: the advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (SODA '07). Society for Industrial and Applied Mathematics, USA, 1027–1035.
Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of explainers of black box deep neural networks for computer vision: A survey. Machine Learning and Knowledge Extraction, 3(4), 966-989.
Ahn, J. K. (2014). Study on images of a city and surface design of public office buildings. Journal of Cultural Product & Design, 37(0), 81-90.
Archisoup (2017). Understanding building yypology. Retrieved April 2, 2023 from https://www.archisoup.com/understanding-building-typology
Arthur, D., & Vassilvitskii, S. (2007). K-means++: the advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (SODA '07). Society for Industrial and Applied Mathematics, USA, 1027–1035.
Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of explainers of black box deep neural networks for computer vision: A survey. Machine Learning and Knowledge Extraction, 3(4), 966-989.
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