CAMHighways Dataset
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The Digital Roads team is proud to present the CAMHighways dataset, built from mobile mapping data that surveyed over 40km of UK Highways.
The dataset consists of textured meshes for road assets (including the pavement, traffic signs and road furniture), segmented and classified point clouds, orthomosaics generated from pavement images, defect label annotations and shapefiles, and ground penetrating radar point clouds. All modalities are georeferenced and can be integrated into game engines and/or GIS software.
The CAMHighways datatset was created to facilitate and automate the building of a Digital Twin (DT), a digital representation of the highway, to streamline inspection and maintenance through virtual reality, robotics simulation, and DT- and AI-driven data analysis. It also serves as a valuable source for other applications, such as training semantic scene understanding and defect detection algorithms.
A paper has been written to introduce the dataset and outline the data preparation process, including novel automation methods developed for this purpose, as well as integration guidelines and possible applications. The paper is available from Advanced Engineering Informatics, Volume 64, March 2025, The paper is available free of charge for a limited time: CAMHighways: The Cambridge Highways dataset
The Digital Roads dataset has been made publicly available to help further investigation into the management of our national infrastructure.
Publicly Accessible dataset
A sample dataset is available through the Digital Roads Project team on request. Please contact: drf-initiative@drf.eng.cam.ac.uk
| Type of data | Size |
|---|---|
| Pointclouds | 76.31GB |
| Orthomosaics | 549.34GB |
| Meshes | 26.16GB |
| Labels | 209.2MB |
| GPR | 36.97GB |
| Pavement Images | 30.68GB |