Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026
- URL: http://arxiv.org/abs/2608.22116v1
- Date: Sat, 22 Aug 2026 21:59:36 GMT
- Title: Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026
- Abstract summary: The dataset provides individual vehicle speed observations on European E-roads tagged in OpenStreetMap as e-road, for the years 2022-2026.<n>Speeds are derived from Copernicus Sentinel-2 Level-2A satellite optical imagery using a processing pipeline.<n>The dataset is distributed as GeoPackage files, with one record per detected vehicle, and can support studies of traffic patterns, speed behavior, transport modeling, and the calibration of road network attributes at a continental scale.
- Score: 39.381642096565834
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The dataset provides individual vehicle speed observations on European E-roads: motorways, trunk roads, primary and secondary roads, as tagged in OpenStreetMap as e-road, for the years 2022-2026. Speeds are derived from Copernicus Sentinel-2 Level-2A satellite optical imagery using a processing pipeline that exploits the short, well-characterized acquisition delays between the blue (B02_10m), green (B03_10m), and red bands (B04_10m) of the Sentinel-2 push-broom instrument. A moving vehicle appears at slightly displaced positions in the three bands, forming a moving echo. The detected displaced intensity peaks are linked into per-vehicle trajectories through a prediction-and-matching procedure. The resulting displacements are converted into ground speeds using publicly accessible inter-band time delays. Each record contains the trajectory geometry, per-channel displacements and headings, internal quality indicators, the estimated speed, the acquisition timestamp, and the source Sentinel-2 product identifier. The dataset is distributed as GeoPackage files, with one record per detected vehicle, and can support studies of traffic patterns, speed behavior, transport modeling, and the calibration of road network attributes at a continental scale.
Related papers
- Intelligent Truck Matching in Full Truckload Shipments using Ping2Hex approach [0.13854111346209866]
Missing or corrupted vehicle identifiers prevent traditional matching approaches, leaving shipments without visibility.<n>This paper presents Intelligent Truck Matching (ITM) 2.0, a machine learning system that formulates matching as a probabilistic ranking problem.<n>ITM 2.0 achieves 26 percentage point precision improvement in North America and 14 points in Europe, while doubling coverage.
arXiv Detail & Related papers (2026-05-08T13:40:53Z) - The Swarm Intelligence Freeway-Urban Trajectories (SWIFTraj) Dataset - Part II: A Graph-Based Approach for Trajectory Connection [19.272192892376328]
This paper proposes a novel graph-based approach for connecting vehicle trajectories captured by a UAV swarm.<n>An undirected graph is constructed to represent flexible UAV layouts, and an automatic time alignment method is developed to estimate optimal time offsets.<n>Results from real-world experiments show that the time alignment error is within three video frames, corresponding to approximately 0.1 s.
arXiv Detail & Related papers (2026-02-25T14:35:24Z) - Measuring Braking Behavior Using Vehicle Tracking and Camera-to-Satellite Homography Rectification [4.61803711540329]
This paper presents an open-source software application for analyzing traffic camera footage, focusing on vehicle behavior and braking events at signalized urban highways.<n>The core innovation is a robust ground-plane homography estimation that links fixed traffic camera views to satellite orthoimagery.<n>All detection and trajectory data are stored in a ClickHouse database for subsequent analysis.
arXiv Detail & Related papers (2026-01-24T18:59:32Z) - Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery [38.22365259129059]
This paper presents a method for detecting and estimating vehicle speeds using PlanetScope SuperDove satellite imagery.
We propose a Keypoint R-CNN model to track vehicle trajectories across RGB bands, leveraging band timing differences to estimate speed.
Results from drone comparison reveal underestimations, with average speeds of 112.85 km/h for satellite data versus 131.83 km/h from drone footage.
arXiv Detail & Related papers (2024-10-04T18:14:07Z) - Vehicle Perception from Satellite [54.07157185000604]
The dataset is constructed based on 12 satellite videos and 14 synthetic videos recorded from GTA-V.
It supports several tasks, including tiny object detection, counting and density estimation.
128,801 vehicles are annotated totally, and the number of vehicles in each image varies from 0 to 101.
arXiv Detail & Related papers (2024-02-01T15:59:16Z) - MSight: An Edge-Cloud Infrastructure-based Perception System for
Connected Automated Vehicles [58.461077944514564]
This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles.
MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction.
Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
arXiv Detail & Related papers (2023-10-08T21:32:30Z) - OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure.
OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes.
We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
arXiv Detail & Related papers (2023-04-20T16:31:22Z) - Correlating sparse sensing for large-scale traffic speed estimation: A
Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging.
We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
arXiv Detail & Related papers (2022-10-21T07:25:57Z) - Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car.
We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
arXiv Detail & Related papers (2021-09-16T13:10:27Z) - A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation,
and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic
Lanes [5.735035463793008]
We present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane.
We use a highly instrumented vehicle to verify the estimated speeds obtained from video inference.
We then use the estimated speeds to calibrate the parameters of a driver behavior model for the vehicles in the area of study.
arXiv Detail & Related papers (2021-06-18T06:15:53Z)
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.