aving access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.
DOCUMENT
The Dutch Environmental Vision and Mobility Vision 2050 promote climate-neutral urban growth around public transport stations, envisioning them as vibrant hubs for mobility, community, and economy. However, redevelopment often increases construction, a major CO₂ contributor. Dutch practice-led projects like 'Carbon Based Urbanism', 'MooiNL - Practical guide to urban node development', and 'Paris Proof Stations' explore integrating spatial and environmental requirements through design. Design Professionals seek collaborative methods and tools to better understand how can carbon knowledge and skills be effectively integrated into station area development projects, in architecture and urban design approaches. Redeveloping mobility hubs requires multi-stakeholder negotiations involving city planners, developers, and railway managers. Designers act as facilitators of the process, enabling urban and decarbonization transitions. CARB-HUB explores how co-creation methods can help spatial design processes balance mobility, attractiveness, and carbon neutrality across multiple stakeholders. The key outputs are: 1- Serious Game for Co-Creation, which introduces an assessment method for evaluating the potential of station locations, referred to as the 4P value framework. 2-Design Toolkit for Decarbonization, featuring a set of Key Performance Indicators (KPIs) to guide sustainable development. 3- Research Bid for the DUT–Driving Urban Transitions Program, focusing on the 15-minute City Transition Pathway. 4- Collaborative Network dedicated to promoting a low-carbon design approach. The 4P value framework offers a comprehensive method for assessing the redevelopment potential of station areas, focusing on four key dimensions: People, which considers user experience and accessibility; Position, which examines the station's role within the broader transport network; Place-making, which looks at how well the station integrates into its surrounding urban environment; and Planet, which addresses decarbonization and climate adaptation. CARB-HUB uses real cases of Dutch stations in transition as testbeds. By translating abstract environmental goals into tangible spatial solutions, CARB-HUB enables scenario-based planning, engaging designers, policymakers, infrastructure managers, and environmental advocates.
The digitalization of railroad infrastructure is aimed at the improvement of maintenance and construction activities. Currently, inspections are done manually, with a domain expert classifying objects. This is a challenging task, considering the Netherlands has more than 3,400 km of railways that need to be inspected and maintained.Research-00764
