Deep Learning Isovist: Unsupervised Spatial Encoding in Architecture

Mikhael Johanes, Jeffrey Huang

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

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 floor plan 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.

Original languageEnglish
Publication statusPublished - 2021
Event2021 Association for Computer Aided Design in Architecture Annual Conference, ACADIA 2021 - Virtual, Online
Duration: 3 Nov 20216 Nov 2021

Conference

Conference2021 Association for Computer Aided Design in Architecture Annual Conference, ACADIA 2021
CityVirtual, Online
Period3/11/216/11/21

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