Journal article 844 views 101 downloads
Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
AI, Volume: 2, Issue: 1, Pages: 135 - 149
Swansea University Authors:
James Flynn, Cinzia Giannetti
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Copyright: © 2021 by the authors. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license
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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...
| Published in: | AI |
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| ISSN: | 2673-2688 |
| Published: |
MDPI AG
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa56687 |
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| 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 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. |
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| Keywords: |
deep learning; electric vehicles; transfer learning; remote sensing; Google Street View |
| College: |
Faculty of Science and Engineering |
| Funders: |
European Social Fund via the Welsh Government (c80816), Engineering and Physical Sciences Research Council (Grant Ref: EP/L015099/1). |
| Issue: |
1 |
| Start Page: |
135 |
| End Page: |
149 |