Dai, M., Ward, W.O.C. orcid.org/0000-0002-4904-7294, Arbabi, H. orcid.org/0000-0001-8518-9022 et al. (2 more authors) (2022) Scalable residential building geometry characterisation using vehicle-mounted camera system. Energies, 15 (16). 6090.
Abstract
Residential buildings are an important sector in the urban environment as they provide essential dwelling space, but they are also responsible for a significant share of final energy consumption. In addition, residential buildings that were built with outdated standards usually face difficulty meeting current energy performance standards. The situation is especially common in Europe, as 35% of buildings were built over fifty years ago. Building retrofitting techniques provide a choice to improve building energy efficiency while maintaining the usable main structures, as opposed to demolition. The retrofit assessment requires the building stock information, including energy demand and material compositions. Therefore, understanding the building stock at scale becomes a critical demand. A significant piece of information is the building geometry, which is essential in building energy modelling and stock analysis. In this investigation, an approach has been developed to automatically measure building dimensions from remote sensing data. The approach is built on a combination of unsupervised machine learning algorithms, including K-means++, DBSCAN and RANSAC. This work is also the first attempt at using a vehicle-mounted data-capturing system to collect data as the input to characterise building geometry. The developed approach is tested on an automatically built and labelled point cloud model dataset of residential buildings and shows capability in acquiring comprehensive geometry information while keeping a high level of accuracy when processing an intact model.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | building dimension measurement; urban building energy modelling; building reconstruction; building stock |
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 The Alan Turing Institute n/a Engineering and Physical Sciences Research Council EP/V012053/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Aug 2022 08:56 |
Last Modified: | 26 Aug 2022 08:56 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/en15166090 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190287 |