Digitalisation in railway networks harnesses digital technologies to optimise operations, leading to enhanced efficiency, and reduced energy consumption. By analysing real-time data, railways can predict maintenance needs, improve passenger experiences, and seamlessly integrate with other transport modes. As societies strive for sustainable transportation solutions, it is imperative to understand and collect digitalisation techniques to enhance efficiency and reduce the ecological footprint of railway networks. This paper serves as a snapshot of the current state of the art addressing the pivotal role of point cloud techniques in advancing railway digitalisation and providing valuable pointers for future research directions. Employing a systematic review approach, our study concentrates exclusively on research centred around railway assets and their digitalisation via point cloud data. We have themed the literature into pre-processing, modelling, and digital twinning. Within this review, we analyse diverse modelling and pre-processing techniques and categorise them for clarity. The digital twin techniques are also collected, though these techniques are scarce in the context of railway infrastructure and point clouds. The paper also presents a compilation of dataset statistics highlighting the scarcity of openly available railway-specific datasets. This scarcity considerably hampers the feasibility of research reproducibility and the comparative analysis of different approaches. Our conclusion reflects on the challenges encountered and proposes a course for future research. Particularly, we conclude that hybrid methodologies that combine machine learning with structure-based techniques hold substantial promise toward creating digital twins, considering the intrinsic characteristics of railway infrastructure.

Having 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.

Research into explainable artificial intelligence (XAI) methods has exploded over the past five years. It is essential to synthesize and categorize this research and, for this purpose, multiple systematic reviews on XAI mapped out the landscape of the existing methods. To understand how these methods have developed and been applied and what evidence has been accumulated through model training and analysis, we carried out a tertiary literature review that takes as input systematic literature reviews published between 1992 and 2023. We evaluated 40 systematic literature review papers and presented binary tabular overviews of researched XAI methods and their respective characteristics, such as the scope, scale, input data, explanation data, and machine learning models researched. We identified seven distinct characteristics and organized them into twelve specific categories, culminating in the creation of comprehensive research grids. Within these research grids, we systematically documented the presence or absence of research mentions for each pairing of characteristic and category. We identified 14 combinations that are open to research. Our findings reveal a significant gap, particularly in categories like the cross-section of feature graphs and numerical data, which appear to be notably absent or insufficiently addressed in the existing body of research and thus represent a future research road map.

In this paper, we discuss the cone of copositive tensors and its approximation. We describe some basic properties of copositive tensors and positive semidefinite tensors. Specifically, we show that a non-positive tensor (or Z-tensor) is copositive if and only if it is positive semidefinite. We also describe cone hierarchies that approximate the copositive cone. These hierarchies are based on the sum of squares conditions and the non-negativity of polynomial coefficients. We provide a compact representation for the approximation based on the non-negativity of polynomial coefficients. As an immediate consequence of this representation, we show that the approximation based on the non-negativity of polynomial coefficients is polyhedral. Furthermore, these hierarchies are used to provide approximation results for optimizing a (homogeneous) polynomial over the simplex.