Ward, W. orcid.org/0000-0002-4904-7294, Dai, M., Arbabi, H. orcid.org/0000-0001-8518-9022 et al. (3 more authors) (2022) Measuring the cityscape : a pipeline from street-level capture to urban quantification. In: Lützkendorf, T., Roswag-Klinge, E., Gundlach, K., Schlez, S., Passer, A. and Habert, G., (eds.) IOP Conference Series : Earth and Environmental Science. sbe Berlin 2022, 20-23 Sep 2022, Berlin, Germany. IOP Publishing
Abstract
Any solution to achieving climate targets must be performed at scale. Data driven methods allow expert modelling to be emulated over a large scope. In the UK, there are nearly 30 million residential properties, contributing to over 30% of the national energy consumption. As part of the UK Government's requirement to meet net-zero emissions by 2050, retrofitting residential buildings forms a significant part of the national strategy. This work addresses the problem of identifying, characterising and quantifying urban features at scale. A pipeline incorporating photogrammetry, automatic labelling using machine learning, and 3-D geometry has been developed to automatically reconstruct and extract dimensional and spatial features of a building from street-level mobile sensing.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Keywords: | building stock; 3-D modelling; street-level capture; computer vision |
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 EP/W037211/1 Engineering and Physical Sciences Research Council EP/V012053/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Sep 2022 08:20 |
Last Modified: | 27 Sep 2022 08:20 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1755-1315/1078/1/012036 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191087 |
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