Generating Railway Geometric Digital Twins from Airborne LiDAR Data

Ariyachandra, Mahendrini Fernando; Brilakis, Ioannis

doi:10.35490/ec3.2021.163

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Similarly, Sresakoolchai et al (2022) [89] employed DL models and a BIM model to predict track geometry parameters. Moreover, in [90], an approach was proposed to build railway track geometric DTs (RailGDT) from airborne LiDAR data. The proposed method leverages the consistency of rail infrastructure to generate Rail-GDTs in a cost-effective and less arduous manner.…”

Section: ) Ai Approaches In Dtrmentioning

confidence: 99%

Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

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Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, this survey clarifies how DT can serve the railway twin system designers and developers. As DTR is still in its early adoption stage, there is hardly any clear direction to identify the technologies for specific DTR applications. Therefore, based on our findings we present a taxonomy for DTR for designers and developers. Finally, we describe potential challenges, pitfalls, and opportunities in DTR for future researchers.

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Section: ) Ai Approaches In Dtrmentioning

confidence: 99%

Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Special care was taken in processing catenary wires. The main contribution of Ariyachandra and Brilakis [15] is the integration of industry foundation classes, which relate to a basic form of rearrangement. However, the rail-bed is modelled as a triangulated surface, and the assets are not c²-continous.…”

Section: Rule-based Segmentationmentioning

confidence: 99%

Enhancing Railway Detection by Priming Neural Networks with Project Exaptations

Eickeler

Borrmann

2022

Remote Sensing

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When integrating railway constructions and refurbishments into an existing infrastructure, it is beneficial to have knowledge of the exact state, geometry, and placement of the connected assets. While new constructions and the maintenance of existing lines can directly use existing digital models and incorporate them into their processes, existing railways often predate digital technologies. This gap in digital models leaves the planning processes of new constructions and refurbishments to primarily rely on non-automated and analogue workflows. With a multitude of asset types, layouts and country-specific standards, the automatic generation of adequate detection models is complicated and needs to be tailored to the current project environment, generating considerable overhead. Addressing this issue, this paper presents the concept of priming. Priming increases the adaptation performance to highly volatile, low-data environments by leveraging previous, existing CAD projects. We introduce a translation scheme that converts the existing 3D models into realistic, project-specific, synthetic surveys and a complemental dialled-in training routine. When applied to a convolutional neural network, we show that the primed training will converge faster and with greater stability, especially when using sparse training data. Our experiments show that priming can reduce the time for network adaptation by over 50%, while also improving resilience to underrepresented object types.

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Digital Twins: Past, Present, and Future

Grieves¹

2023

The Digital Twin

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Generating Railway Geometric Digital Twins from Airborne LiDAR Data

Cited by 4 publications

References 36 publications

Digital Twin for Railway: A Comprehensive Survey

Digital Twin for Railway: A Comprehensive Survey

Enhancing Railway Detection by Priming Neural Networks with Project Exaptations

Digital Twins: Past, Present, and Future

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