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Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

James Flynn, Cinzia Giannetti Orcid Logo

AI, Volume: 2, Issue: 1, Pages: 135 - 149

Swansea University Authors: James Flynn, Cinzia Giannetti Orcid Logo

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DOI (Published version): 10.3390/ai2010009

Abstract

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning...

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Published in: AI
ISSN: 2673-2688
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56687
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first_indexed 2021-04-19T10:52:10Z
last_indexed 2023-01-11T14:36:04Z
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spelling 2022-08-15T15:09:49.1593577 v2 56687 2021-04-19 Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging 90788c8b9c1334834ba9cc37403ea471 James Flynn James Flynn true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2021-04-19 FGSEN With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment. Journal Article AI 2 1 135 149 MDPI AG 2673-2688 deep learning; electric vehicles; transfer learning; remote sensing; Google Street View 16 3 2021 2021-03-16 10.3390/ai2010009 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU College/Department paid the OA fee European Social Fund via the Welsh Government (c80816), Engineering and Physical Sciences Research Council (Grant Ref: EP/L015099/1). 2022-08-15T15:09:49.1593577 2021-04-19T11:49:44.7865646 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering James Flynn 1 Cinzia Giannetti 0000-0003-0339-5872 2 56687__19677__132982e0e61a4c15b56812167e970cf1.pdf 55687.pdf 2021-04-19T11:51:40.2948290 Output 3697085 application/pdf Version of Record true Copyright: © 2021 by the authors. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license true eng http://creativecommons.org/licenses/by/4.0/
title Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
spellingShingle Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
James Flynn
Cinzia Giannetti
title_short Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
title_full Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
title_fullStr Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
title_full_unstemmed Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
title_sort Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
author_id_str_mv 90788c8b9c1334834ba9cc37403ea471
a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv 90788c8b9c1334834ba9cc37403ea471_***_James Flynn
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti
author James Flynn
Cinzia Giannetti
author2 James Flynn
Cinzia Giannetti
format Journal article
container_title AI
container_volume 2
container_issue 1
container_start_page 135
publishDate 2021
institution Swansea University
issn 2673-2688
doi_str_mv 10.3390/ai2010009
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
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description With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.
published_date 2021-03-16T04:11:49Z
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score 11.016258

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