Proactive road maintenance extends asset lifespan, enhances safety and reduces downtime. However, costly reactive maintenance becomes necessary without up-to-date and well-structured data. While Geometric Digital Twins (GDT) offer digital replicas of physical structures that can be used to automate maintenance activities, no automated tools are currently available for GDT’s upkeep. This paper addresses this issue and proposes a method with a multi-step pipeline for detecting changes, matching instances, identifying newly added objects and applying these changes to the GDT 3D model using point clouds. Our methods, namely Iterative Change Refinement using a single labelled scan and Dual Instance Aware Change Retrieval using two scans, achieve a 0.89-0.97 F1 score in change detection, and our holistic pipeline results in less than 0.1° rotation angle and 0.01 m translation mean squared errors. This pipeline automates the process of GDT maintenance, making such digital twins viable and practically applicable to the industries.

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. • The new CAMHighways dataset is presented, spanning 42.8 km of UK highways. • Mobile mapping data is prepared for building a road DT for inspection & maintenance. • 3D meshes, point clouds, pavement orthomosaics, labels, and GPR data are included. • Several aspects of the DT generation process are automated. • The dataset is integrated into game engines and GIS software.