| id |
ecaade2022_158 |
| authors |
Zhao, Xingjian, Wang, Tsung-Hsien and Peng, Chengzhi |
| year |
2022 |
| title |
Automatic Room Type Classification using Machine Learning for Two-Dimensional Residential Building Plans |
| source |
Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 593–600 |
| doi |
https://doi.org/10.52842/conf.ecaade.2022.2.593
|
| summary |
Building plan semantic retrieval is of interest in every stage of construction and facility management processes. A conceptual design model with a space layout can be used for the early building evaluation, such as functional spatial validation, circulation and security checking, cost estimation, and preliminary energy consumption simulation. With the development of information technology, existing machine learning methods applied to semantic segmentation of building plan images have successfully identified building elements such as doors, windows, and walls. However, for the higher level of room type/function recognition, the prediction accuracy is low when building plans do not contain sufficient details such as furniture. In this paper, we present a workflow and a predictive model for residential room type classification. Given a building plan image, the building elements are first identified, followed by room feature extraction by connectivity and morphological characterization using a rule-based algorithm. The Multi-Layer Perceptron (MLP) is trained with the feature set and then predicts the room type of test samples. We collected 1,586 residential room samples from 165 building layout plans and categorized rooms into nine types. Finally, our current model can achieve a classification accuracy of 0.82. |
| keywords |
Floor Plan Semantic Retrieval, Room Type Classification, Machine Learning |
| series |
eCAADe |
| email |
xzhao66@sheffield.ac.uk |
| full text |
 file.pdf (1,153,602 bytes) |
| references |
Content-type: text/plain
|
| last changed |
2024/04/22 07:10 |
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