| id |
acadia21_134 |
| authors |
Johanes, Mikhael; Huang, Jeffrey |
| year |
2021 |
| title |
Deep Learning Isovist |
| source |
ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 134-141. |
| doi |
https://doi.org/10.52842/conf.acadia.2021.134
|
| summary |
Understanding the qualitative aspect of space is essential in architectural design. However, the development of computational design tools has lacked features to comprehend architectural quality that involves perceptual and phenomenological aspects of space. The advancement in machine learning opens up a new opportunity to understand spatial qualities as a data-driven approach and utilize the gained information to infer or derive the qualitative aspect of architectural space. This paper presents an experimental unsupervised encoding framework to learn the qualitative features of architectural space by using isovist and deep learning techniques. It combines stochastic isovist sampling with Variational Autoencoder (VAE) model and clustering method to learn and extract spatial patterns from thousands of floorplans data. The developed framework will enable the encoding of architectural spatial qualities into quantifiable features to improve the computability of spatial qualities in architectural design. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (3,709,612 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/10/22 12:06 |
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