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Scalable Residential Building Geometry Characterisation Using Vehicle-Mounted Camera System

Author

Listed:
  • Menglin Dai

    (Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, UK)

  • Wil O. C. Ward

    (Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, UK)

  • Hadi Arbabi

    (Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, UK)

  • Danielle Densley Tingley

    (Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, UK)

  • Martin Mayfield

    (Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, UK)

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 In addition, residential buildings that were built with outdated standards usually face difficulty meeting current energy performance standards.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.

Suggested Citation

  • Menglin Dai & Wil O. C. Ward & Hadi Arbabi & Danielle Densley Tingley & Martin Mayfield, 2022. "Scalable Residential Building Geometry Characterisation Using Vehicle-Mounted Camera System," Energies, MDPI, vol. 15(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6090-:d:894763
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    References listed on IDEAS

    as
    1. Lingfors, D. & Bright, J.M. & Engerer, N.A. & Ahlberg, J. & Killinger, S. & Widén, J., 2017. "Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis," Applied Energy, Elsevier, vol. 205(C), pages 1216-1230.
    2. Gooding, James & Crook, Rolf & Tomlin, Alison S., 2015. "Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy, Elsevier, vol. 148(C), pages 93-104.
    3. Tiziano Dalla Mora & Lorenzo Teso & Laura Carnieletto & Angelo Zarrella & Piercarlo Romagnoni, 2021. "Comparative Analysis between Dynamic and Quasi-Steady-State Methods at an Urban Scale on a Social-Housing District in Venice," Energies, MDPI, vol. 14(16), pages 1-22, August.
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