Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation
Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation
复制标题
让绿色交通成为现实：基于分类的数据分析方法来识别适合电动汽车充电站安装的属性
DOI：
10.1109/igarss47720.2021.9553748
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
C. Giannetti
中科院分区：
文献类型：
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作者：
J. Flynn;E. Brealy;C. Giannetti
随着电动汽车（EV）成为英国绿色交通的主导模式，地方当局和城市规划者能够准确地绘制现有EV基础设施的地图至关重要。在本文中，我们展示了一种新的数据处理管道来分析遥感图像数据，以突出最适合电动汽车基础设施的城市区域。通过将深度迁移学习应用于多个数据集，我们能够识别适合安装家用电动汽车充电点的各个地址。使用同样的方法，我们还强调了社区充电站将最有效地安装的区域。我们通过整合地形数据、人口普查数据和遥感图像数据来改进以前的方法，以实现能够大规模测量外部建筑特征的全自动系统。
With Electric Vehicles (EVs) emerging as the dominant mode of green transportation in the UK, it is critical that local authorities and urban planners can accurately map the existing EV infrastructures in place. In this paper, we demonstrate a novel data processing pipeline to analyse remotely sensed image data to highlight areas of a city most suitable for EV infrastructure. By applying deep transfer learning to multiple datasets, we are able to identify individual addresses suitable for the installation of home EV charging points. Using this same methodology, we also highlight areas where community charging points would be most effectively installed. We improve on previous methods by integrating topographical data, Census data, and remotely sensed image data to achieve a fully automated system capable of large-scale surveying of external building characteristics.