A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing
This addresses the need for climate-resilient EV infrastructure planning, though it is incremental as it applies existing methods like spatial graph analytics to a new domain.
The paper tackled the problem of assessing the resilience of EV charging infrastructure under environmental and infrastructural stress by introducing RSERI-EV, a multi-modal risk assessment framework that integrates remote sensing and spatial analytics, applied to Wales to generate composite Resilience Scores for charging stations.
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.