Dai, M., Ward, W.O.C., Meyers, G. et al. (2 more authors) (2021) Residential building facade segmentation in the urban environment. Building and Environment, 199. 107921. ISSN 0360-1323
Abstract
Building retrofit is an important facet in the drive to reduce global greenhouse gas emissions. However, delivering building retrofit at scale is a significant challenge, especially in how to automate the process of building surveying. On-site survey by expert surveyors is the main approach in the industry. This can lead to a high workload if planning retrofit at a large-scale. An advanced vehicle-mounted data capturing system has been built to collect urban environmental multi-spectral data. The data contains substantial information that is essential in identifying building retrofit needs. Although the data capturing system is able to collect data in a highly-efficient manner, the data analysis is still a big data challenge to apply the system into delivering building retrofit plans. In this paper, a street-view building facade image segmentation model is designed as the foundation of the holistic data analysis framework. The model is developed on the deep learning-based semantic segmentation technology and uses an ensemble learning strategy. The object detection technology is fused into the model as an magnifier to improve the model performance on small objects and boundary predictions. The model has achieved state-of-the-art levels of accuracy on a built street-view building facade image dataset.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Building and Environment. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Building retrofit; Building facade; Deep learning; Environmental modelling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/P016782/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/R013411/1 Engineering and Physical Sciences Research Council EP/S016627/1 Engineering and Physical Sciences Research Council EP/V012053/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 May 2021 16:29 |
Last Modified: | 27 Apr 2022 00:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.buildenv.2021.107921 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173692 |
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