A multi-modal spatial risk framework for EV charging infrastructure using remote sensing

Karakuş, Oktay ORCID: https://orcid.org/0000-0001-8009-9319 and Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 2026. A multi-modal spatial risk framework for EV charging infrastructure using remote sensing. Presented at: International Workshops of ECML PKDD 2025, Porto, Portugal, 15–19 September 2025. Published in: Koprinska, I., Mendes-Moreira, J. and Branco, P. eds. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Communications in Computer and Information Science. 249 Cham: Springer, pp. 249-259. 10.1007/978-3-032-19108-3_17

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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 (kNN) 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.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Springer
ISBN:	9783032191076
ISSN:	1865-0929
Last Modified:	18 May 2026 10:16
URI:	https://orca.cardiff.ac.uk/id/eprint/187045

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