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

• URL: http://arxiv.org/abs/2506.19860v1
• Date: Tue, 10 Jun 2025 05:27:51 GMT
• Title: A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing
• Abstract summary: RSERI-EV is a spatially explicit and multi-modal risk assessment framework.<n>It combines remote sensing data, open infrastructure datasets, and spatial graph analytics to evaluate the vulnerability of EV charging stations.<n>Our prototype highlights the value of multi-source data fusion and interpretable spatial reasoning in supporting climate-resilient, infrastructure-aware EV deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
  VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
  arXiv  Detail & Related papers  (2026-09-02T11:32:52Z)
• Urban Power Grid Topology and Hierarchy Identification from Open Data [53.468443050795344]
  This paper develops an open-data-driven framework for identification of urban power grid topology.<n>We fuse public infrastructure data (power-lines, substations, transformers, poles) to map the high and medium-voltage skeleton.<n>We then leverage geospatial machine learning on OpenStreetMap to group power demand clusters.<n>We reconstruct the complete grid topology that connects 7,330 buildings and all major electricity infrastructure assets.
  arXiv  Detail & Related papers  (2026-06-19T11:51:38Z)
• Spatio-temporal modelling of electric vehicle charging demand [0.41998444721319217]
  We introduce a novel-scale longitudinal dataset collected across Scotland (2022 2025)<n>We formulate EV charging demand as a latent latent latent Gaussian field perform approximate inference via Integrated Nested Laplace Approximation (INLA)<n>On station-level forecasting tasks, our approach achieves competitive predictive accuracy against machine learning baselines, while additionally providing principled uncertainty quan tification and interpretable spatial and temporal decompositions properties that are essential for risk-aware infrastructure planning.
  arXiv  Detail & Related papers  (2026-04-21T09:52:22Z)
• Spatio-Temporal Graph Convolutional Networks for EV Charging Demand Forecasting Using Real-World Multi-Modal Data Integration [1.83159883923531]
  Transportation remains a major contributor to greenhouse gas emissions.<n>This study introduces TW-GCN, a-temporal forecasting framework that combines Graph Conal Networks with temporal architectures.<n>We utilize real-world traffic flows, weather conditions, and proprietary data to capture both spatial dependencies and temporal dynamics.
  arXiv  Detail & Related papers  (2025-10-10T06:31:17Z)
• An Automated Framework for Assessing Electric Vehicle Charging Impacts on a Campus Distribution Grid [0.6978367196609415]
  This paper introduces a unified and automated framework designed to assess the impact of electric vehicle (EV) charging on distribution feeders and transformers at California State University, Northridge (CSUN)<n>Our main contribution is the development of a flexible testbed that integrates Julia, a high-performance programming language for technical computing, with PowerWorld Simulator via the EasySimauto.jl package.<n>The proposed system offers a valuable tool for evaluating transformer loading, feeder utilization, and overall system stress.
  arXiv  Detail & Related papers  (2025-09-09T01:17:59Z)
• Optimizing Electric Vehicle Charging Station Locations: A Data-driven System with Multi-source Fusion [2.993678682876725]
  We develop a data-driven system based on existing EV trips in New South Wales (NSW) state, Australia.<n>Our system integrates data sources including EV trip data, geographical data such as route data and Local Government Area (LGA) boundaries.<n>The outcome of this work can provide a platform for discussion to develop new insights that could be used to give guidance on where to position future EV charging stations.
  arXiv  Detail & Related papers  (2025-04-18T07:10:48Z)
• ChatBEV: A Visual Language Model that Understands BEV Maps [58.3005092762598]
  We introduce ChatBEV-QA, a novel BEV VQA benchmark containing over 137k questions.<n>This benchmark is constructed using a novel data collection pipeline that generates scalable and informative VQA data for BEV maps.<n>We propose a language-driven traffic scene generation pipeline, where ChatBEV facilitates map understanding and text-aligned navigation guidance.
  arXiv  Detail & Related papers  (2025-03-18T06:12:38Z)
• SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset [101.51012770913627]
  Bird's-eye view (BEV) perception has garnered significant attention in autonomous driving in recent years.<n>SimBEV is a randomized synthetic data generation tool that is extensively scalable and scalable.<n>SimBEV is used to create the SimBEV dataset, a large collection of annotated perception data from diverse driving scenarios.
  arXiv  Detail & Related papers  (2025-02-04T00:00:06Z)
• Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas [1.1434534164449743]
  This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data.<n>Our model focuses on predicted peak power demand and daily load, providing insights into charging behavior.
  arXiv  Detail & Related papers  (2024-12-13T19:55:19Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System [5.600639345303369]
  We present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions.
  arXiv  Detail & Related papers  (2023-04-10T23:01:02Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Defining a synthetic data generator for realistic electric vehicle charging sessions [6.37470346908743]
  Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Yet, the limited availability of such EV sessions data hinders further development in these fields. We develop a synthetic data generator for EV charging sessions.
  arXiv  Detail & Related papers  (2022-02-28T11:18:40Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets [7.2775693810940565]
  Public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs) Because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. We present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets.
  arXiv  Detail & Related papers  (2020-04-10T16:31:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.