No Cover Image

Journal article 86 views 21 downloads

Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging / James Flynn, Cinzia Giannetti

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

Swansea University Authors: James Flynn, Cinzia Giannetti

  • 55687.pdf

    PDF | Version of Record

    Copyright: © 2021 by the authors. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license

    Download (3.53MB)

Check full text

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...

Full description

Published in: AI
ISSN: 2673-2688
Published: MDPI AG 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56687
Tags: Add Tag
No Tags, Be the first to tag this record!
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.
Keywords: deep learning; electric vehicles; transfer learning; remote sensing; Google Street View
College: College of Engineering
Issue: 1
Start Page: 135
End Page: 149