用遥感数据评估充电桩在极端环境下的脆弱性。
A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing
- 融合遥感与地理数据，构建多模态风险评估框架。
- 在威尔士实测中识别出高风险充电桩分布区域。
- 适合关注气候韧性与智能基建的政策制定者。
电动汽车充电基础设施对可持续交通系统日益重要，但其在环境与基础设施压力下的韧性仍缺乏研究。本文提出RSERI-EV，一种基于遥感数据、开放基础设施数据集与空间图分析的显式空间多模态风险评估框架，用于评估充电桩的脆弱性。该框架整合洪水风险图、地表温度（LST）极值、植被指数（NDVI）、土地利用/覆被（LULC）、邻近变电站距离及道路可达性等多源数据，生成综合韧性评分。通过构建充电网络的$k$-最近邻（$k$NN）空间图，实现邻域比较与图感知诊断。在威尔士充电桩数据集上的原型验证表明，多源数据融合与可解释的空间推理对支持气候韧性、基础设施敏感型的电动车部署具有重要价值。
原文摘要 · Abstract (English)
Electric vehicle (EV) charging infrastructure is increasingly critical to sustainable transport systems, yet its resilience under environmental and infrastructural stress remains underexplored. In this paper, we introduce RSERI-EV, a spatially explicit and multi-modal risk assessment framework that combines remote sensing data, open infrastructure datasets, and spatial graph analytics to evaluate the vulnerability of EV charging stations. RSERI-EV integrates diverse data layers, including flood risk maps, land surface temperature (LST) extremes, vegetation indices (NDVI), land use/land cover (LULC), proximity to electrical substations, and road accessibility to generate a composite Resilience Score. We apply this framework to the country of Wales EV charger dataset to demonstrate its feasibility. A spatial $k$-nearest neighbours ($k$NN) graph is constructed over the charging network to enable neighbourhood-based comparisons and graph-aware diagnostics. Our prototype highlights the value of multi-source data fusion and interpretable spatial reasoning in supporting climate-resilient, infrastructure-aware EV deployment.
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