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.
Highlights
AI 2021, 2, 135–149. https://Machine learning and computer vision algorithms have proven to be powerful tools for classification of remotely sensed imagery
With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we address this research gap in order to better understand existing infrastructures in place to support the uptake of these vehicles
We have demonstrated that surveying external building characteristics of small to medium urban areas is possible using open source data
Summary
Machine learning and computer vision algorithms have proven to be powerful tools for classification of remotely sensed imagery. The rapid development of smart devices, artificial neural networks, and computer vision has meant recent approaches to image classification in remote sensing rely increasingly on machine learning techniques [1,2,3,4]. Computer vision algorithms have proven to be especially powerful tools due to their versatility, scalability, and low cost [5]. Despite the large amount of research into Land Use and Land Cover (LULC) classification using remote sensing imagery, as well as considerable research efforts into planning smart grid infrastructures for future smart cities [6], the identification and classification of private off-street parking represents a gap in the literature.
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