CAMHighways: The Cambridge Highways dataset

Marie d'Avigneau, A; Potseluyko, L; Anvo, NR; Taha, HM; Reja, VK; Davletshina, D; Lam, P; de Silva, L; Al-Tabbaa, A; Brilakis, I

CAMHighways: The Cambridge Highways dataset

Marie d'Avigneau, A Potseluyko, L Anvo, NR Taha, HM Reja, VK Davletshina, D Lam, P de Silva, L Al-Tabbaa, A Brilakis, I

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Advanced Engineering Informatics, 2025, 64
Issue Date:: 2025-03-01

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Field	Value	Language
dc.contributor.author	Marie d'Avigneau, A
dc.contributor.author	Potseluyko, L
dc.contributor.author	Anvo, NR
dc.contributor.author	Taha, HM
dc.contributor.author	Reja, VK
dc.contributor.author	Davletshina, D
dc.contributor.author	Lam, P
dc.contributor.author	de Silva, L
dc.contributor.author	Al-Tabbaa, A
dc.contributor.author	Brilakis, I
dc.date.accessioned	2026-02-09T03:49:53Z
dc.date.available	2026-02-09T03:49:53Z
dc.date.issued	2025-03-01
dc.identifier.citation	Advanced Engineering Informatics, 2025, 64
dc.identifier.issn	1474-0346
dc.identifier.issn	1873-5320
dc.identifier.uri	http://hdl.handle.net/10453/193069
dc.description.abstract	The CAMHighways dataset is presented, built from mobile mapping data that surveyed over 40 km 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 main aim of this work is to facilitate and automate the building of a Digital Twin (DT), a digital representation of the highway, in order 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. This paper introduces the dataset and outlines the data preparation process, including novel automation methods developed for this purpose, as well as integration guidelines and possible applications.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Advanced Engineering Informatics
dc.relation.isbasedon	10.1016/j.aei.2024.103036
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Design Practice & Management
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	CAMHighways: The Cambridge Highways dataset
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-09T03:49:51Z
pubs.publication-status	Published
pubs.volume	64

Abstract:

The CAMHighways dataset is presented, built from mobile mapping data that surveyed over 40 km 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 main aim of this work is to facilitate and automate the building of a Digital Twin (DT), a digital representation of the highway, in order 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. This paper introduces the dataset and outlines the data preparation process, including novel automation methods developed for this purpose, as well as integration guidelines and possible applications.

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http://hdl.handle.net/10453/193069