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arXiv · 2506.19860

A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing

Oktay Karakuş·Padraig Corcoran
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Abstract

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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Oktay Karakuş, Padraig Corcoran. 2025-06-10. A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing. https://arxiv.org/abs/2506.19860

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