| id |
cf2019_014 |
| authors |
Ferrando, Cecilia; Niccolo Dalmasso, Jiawei Mai, Daniel Cardoso Llach |
| year |
2019 |
| title |
Architectural Distant Reading Using Machine Learning to Identify Typological Traits Across Multiple Buildings |
| source |
Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 114-127 |
| summary |
This paper introduces an approach to architectural “distant reading”: the use of computational methods to analyze architectural data in order to derive spatial insights from—and explore new questions concerning—large collections of architectural work. Through a case study comprising a dataset of religious buildings, we show how we may use machine learning techniques to identify typological and functional traits from building plans. We find that spatial structure, rather than local features, is particularly effective in supporting this type of analysis. Further, we speculate on the potential of this computational method to enrich architectural design, research, and criticism by, for example, enabling new ways of thinking about architectural concepts such as typology in ways that reflect gradual variations, rather than sharp distinctions. |
| keywords |
Architectural Analytics, Machine Learning, Classification, Religious buildings, Space Syntax |
| series |
CAAD Futures |
| email |
|
| full text |
 file.pdf (1,523,385 bytes) |
| references |
Content-type: text/plain
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| last changed |
2019/07/29 14:08 |
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