| id |
caadria2021_086 |
| authors |
Eisenstadt, Viktor, Arora, Hardik, Ziegler, Christoph, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas |
| year |
2021 |
| title |
Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 191-200 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.191
|
| summary |
The main aim of this research is to harness deep learning techniques to support architectural design problems in early design phases, for example, to enable auto-completion of unfinished designs. For this purpose, we investigate the possibilities offered by established deep learning libraries such as TensorFlow. In this paper, we address a core challenge that arises, namely the transformation of semantic building information into a tensor format that can be processed by the libraries. Specifically, we address the representation of information about room types of a building and type of connection between the respective rooms. We develop and discuss five formats. Results of an initial evaluation based on a classification task show that all formats are suitable for training deep learning networks. However, a clear winner could be determined as well, for which a maximum value of 98% for validation accuracy could be achieved. |
| keywords |
deep learning; spatial configuration; data representation; semantic building fingerprint |
| series |
CAADRIA |
| email |
viktor.eisenstadt@dfki.de |
| full text |
 file.pdf (979,095 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:55 |
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