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Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site's specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research. [ABSTRACT FROM AUTHOR]

Reconstructing three-dimensional (3D) models of power equipment plays an increasingly important role in advancing digital twin power grids. To reconstruct a high-precision model, it is crucial to accurately obtain the pylon type and its necessary parameter information before modeling. This study proposes an improved method for identifying pylon types based on similarity measurement and a linearly transformed dataset. It begins by simplifying the identification of point clouds using the pylon shape curve. Subsequently, the resemblance between the curve and those curves within the dataset is evaluated using a similarity measurement to determine the pylon type. A novel method is proposed for calculating the characteristic parameters of the pylon point clouds. The horizontal and vertical distribution characteristics of the pylon point clouds are analyzed to identify key segmentation positions based on their types. Feature points are derived from key segmentation positions to calculate the characteristic parameters. Finally, the pylon 3D models are reconstructed on the basis of the calculated values. The experimental results showed that, compared with other similarity measurements, the Hausdorff distance had the best effect as a similarity measurement using the linearly transformed dataset, with an overall evaluation F-score of 86.4%. The maximum relative error of the calculated pylon parameters did not exceed 5%, affirming the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The benefits of having digital twins of urban buildings are numerous. However, a major difficulty encountered in their creation from airborne LiDAR point clouds is the effective means of accurately reconstructing significant occlusions amidst point density variations and noise. To bridge the noise/sparsity/occlusion gap and generate high fidelity 3D building models, we propose APC2Mesh which integrates point completion into a 3D reconstruction pipeline, enabling the learning of dense geometrically accurate representation of buildings. Specifically, we leveraged complete points generated from occluded ones as input to a linearized skip attention-based deformation network for 3D mesh reconstruction. In our experiments, conducted on 3 different scenes, we demonstrate that: (1) Compared to SoTA methods like NDF, Points2Poly, Point2Mesh, and a few others, APC2Mesh ranked second in positional RMSE and first in directional RSME, with error magnitudes of 0.0134 m and 0.1581, respectively. This indicates the efficacy of APC2Mesh in handling the challenges of airborne building points of diverse styles and complexities. (2) The combination of point completion with typical deep learning-based 3D point cloud reconstruction methods offers a direct and effective solution for reconstructing significantly occluded airborne building points. As such, this neural integration holds promise for advancing the creation of digital twins for urban buildings with greater accuracy and fidelity. Our source code is available at https://github.com/geospatial-lab/APC2Mesh. [ABSTRACT FROM AUTHOR]

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines. [ABSTRACT FROM AUTHOR]

This document discusses the role of sensing technology in driving innovation in digital technology, particularly in areas such as data acquisition, machine learning, digital twins, sustainable material-based electronics, and human-machine interaction. It explains that digital twins are multiphysical, multiscale, and probabilistic simulations of complex products in both physical and virtual environments. The use of sensors is crucial in building digital twins, as they collect data and synchronize physical objects with their virtual representations. The document also highlights various applications of sensing technology in fields such as automatic driving, smart cities, medical care, and intelligent robots. The document features eight research and review articles that explore the recent advances in physical sensing and its applications in machine learning, human-machine interfaces, data confusion, and digital twin innovation. These articles cover topics such as evaluating shallow-water bathymetry, magnetic induction switches, surface-enhanced Raman spectroscopic sensors, gear fault detection, cable defect identification, monitoring systems for intensive care units, fake-news detection, and virtual reality online education. The authors express their gratitude to the contributors and reviewers of this special issue and hope that the articles inspire new innovations in the field. [Extracted from the article]

Digital modelling stands as a pivotal step in the realm of Digital Twinning. The future trend of Digital Twinning involves automated exploration and environmental modelling in complex scenes. In our study, we propose an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The approach allows for dynamic changes to expected targets and behaviours. The evaluation is conducted on a robotic platform with a lightweight 3D LiDAR sensor model. The robustness of different types of odometry is compared, and the impact of parameters on motion planning is explored. The consistency and efficiency of exploring completely unknown areas are assessed in both indoor and outdoor scenarios. The experiment shows that the method proposed in this article can complete autonomous exploration and environmental modelling tasks in complex indoor and outdoor scenes. Finally, the study concludes by summarizing the reasons for exploration failures and outlining future focuses in this domain. [ABSTRACT FROM AUTHOR]

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas. [ABSTRACT FROM AUTHOR]

Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network's learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network's knowledge. Two deep learning techniques, "RandLaNet" and "KPConv", were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach. [ABSTRACT FROM AUTHOR]

The emergence of Digital Twins in city planning and management marks a contemporary trend, elevating the realm of 3D modeling and simulation for cities. In this context, the use of semantic point clouds to generate 3D city models for Digital Twins proves instrumental in addressing this evolving need. This article introduces a processing pipeline for the automatic modeling of buildings, roads, and vegetation based on the semantic segmentation results of 3D LiDAR point clouds. It employs a semantic segmentation approach that integrates multiple training datasets to achieve precise extraction of target objects. Open-source reconstruction tools have been adapted for building and road modeling, while a Python code was optimized for tree modeling, leveraging a foundational code. The case study was conducted in the city of Liège, Belgium. The obtained results were satisfactory, and the schemas and geometry of the developed models were validated. An evaluation of the adopted reconstruction methods was conducted, along with their comparison to other methods from the literature. [ABSTRACT FROM AUTHOR]

Research on handheld LiDAR data has recently been proliferated due to the emergence of digital twins and indoor mapping. However, most of the existing studies cannot be reproduced in another computational environment. Computational reproducibility requires data, code/software, and computational environment (e.g. versions, settings, etc.) to be openly available. Although there are an increasing number of researches that contribute towards open data, there are still few studies investigating the remaining two aspects. One of the common tasks in digital twin research is the estimation of indoor space areas. This paper contributes to the computational reproducibility of estimating the area of indoor spaces on a handheld LiDAR dataset using the DBSCAN algorithm. The collected dataset -representing the Geomatics Engineering Department of Hacettepe University, code, and the computational environment was made openly available to satisfy the requirements of computational reproducibility. Three different experiments have been carried out: i) identification of the optimal DBSCAN parameter values for a single indoor space, ii) evaluating to what extent these values are applicable to other rooms, and iii) investigating the effect of room enter/exit times on the estimated room sizes. The main finding of this paper is that the simple consideration of an open-door, which reduces data collection time, the uncertainty of a wall's coordinates, and imperfect choice of DBSCAN parameters, may substantially increase the estimated indoor space size ranging between approximately 40% to 300%. Consequently, relying solely on the DBSCAN algorithm for indoor space area estimation should not be considered as a valid approach. [ABSTRACT FROM AUTHOR]

This study investigates the capture of digital data and the development of models for structures with incomplete documentation and plans. LiDAR technology is utilized to obtain the point clouds of a pedestrian bridge structure. Two different point clouds with varying densities, (i) fine (11 collection locations) and (ii) coarse (4 collection locations), collected via terrestrial LiDAR, are analyzed to generate geometry and structural sections. This geometry is compared to the structural plans, which are then converted into numerical models (finite element—FE model) based on the point cloud data. Point cloud-based FE models (based on fine and coarse data) are compared with the structural plan-based FE model. It is observed that the static and dynamic responses are comparable within an acceptable range of a maximum difference of 5.5% for static deformation and an 8.23% frequency difference, with an average difference of less than 5%. Additionally, the dynamic properties of the fine and coarse point cloud FE models are compared with the operational modal analysis data obtained from the bridge. The fine and course point-cloud-based FE models, without model calibration, achieve an average accuracy of 8.76% and 9.94% for natural frequencies and a 0.89 modal assurance criterion value. The research found that the digital data generation yields promising results in this case for a bridge if documentation or plans are unavailable. With recent technologies and approaches such as digital twins, the connection between physical and virtual entities needs to be established by fusing digital models, sensorial information, and other data forms for better infrastructure management. Models such as those investigated and discussed in this paper can assist engineers with structural preservation in conjunction with monitoring data and utilization for digital twins. [ABSTRACT FROM AUTHOR]

The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM. [ABSTRACT FROM AUTHOR]

The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a digital twin of a subterranean utility tunnel was created, and spatial objects were identified using inbuilt image sensors. The novelty lies in the development of a unique algorithm that breaks down the structure of the utility tunnel into points, lines, and planes, identifying objects using a multimodal image sensor that incorporates light detection and ranging (LiDAR) technology. The three main conclusions of this study are the following: First, a digital twin of the utility tunnel was constructed using building information modeling integrated with a geographic information system (BIM-GIS). Second, a method for extracting spatial objects was defined. Third, image-sensor-based segmentation and a random sample consensus (RANSAC) were applied. In this process, the supplementary algorithm for extracting and updating 3D spatial objects was analyzed and improved. The developed algorithm was tested using point cloud data, showing easier object classification with more precise LiDAR data. [ABSTRACT FROM AUTHOR]

Automated extraction of key points from three-dimensional (3D) point clouds in transmission corridors provides technical support for digital twin construction and risk management of the power grid. However, accurately and efficiently segmenting the point clouds of transmission corridors remains a challenging problem. Traditional segmentation methods for transmission corridors suffer from low accuracy and poor generalization ability, and the potential of deep learning in this field has been overlooked. Therefore, the PointNet++ deep learning model is employed as the backbone network for the segmentation of 3D point clouds in transmission corridors. Additionally, given the distinct distribution of key components, an end-to-end CA-PointNet++ architecture is proposed by integrating the Coordinate Attention (CA) module with PointNet++. This approach captures long-distance spatial contextual features and improves feature saliency for more precise segmentation. Furthermore, CA-PointNet++ is evaluated on a dataset of 3D point clouds collected by unmanned aerial vehicles (UAV) equipped with Light Detection and Ranging (LiDAR) for inspecting transmission corridors. The results show that CA-PointNet++ achieved 93.7% overall accuracy (OA) and 67.4% mean Intersection over Union (mIoU). Comparative studies with established deep learning models confirm that our proposed CA-PointNet++ exhibits high accuracy and strong generalization ability for point cloud segmentation tasks in transmission corridors. [ABSTRACT FROM AUTHOR]

Structural damage is inherent in civil engineering structures, and bridges are no exception. It is vital to monitor and keep track of damage in bridge structures as a result of multiple mechanical, environmental and traffic-induced factors. Monitoring the formation and propagation of structural damage is also pertinent for enhancing the service life of bridges. Bridge health monitoring (BHM) has always been an active research area for engineers and stakeholders. While all monitoring techniques intend to provide accurate and decisive information on the remaining useful life, safety, integrity and serviceability of bridges, maintaining uninterrupted operation of a bridge relies strongly on understanding the development and propagation of damage. BHM methods have been extensively researched on bridges over the decades, and new methodologies have started to be used by domain experts, especially within the last decade. Emerging methods, as the products of technology advancements, have resulted in handy tools that have been quickly adopted by bridge engineers. State-of-the-art techniques such as lidar, photogrammetry, virtual reality, augmented reality, digital twins, computer vision, machine learning and deep learning are now integrated parts of the new-generation of BHM operations. This paper presents a brief overview of these latest BHM technologies. [ABSTRACT FROM AUTHOR]

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

The 3D semantic segmentation of a LiDAR point cloud is essential for various complex infrastructure analyses such as roadway monitoring, digital twin, or even smart city development. Different geometric and radiometric descriptors or diverse combinations of point descriptors can extract objects from LiDAR data through classification. However, the irregular structure of the point cloud is a typical descriptor learning problem—how to consider each point and its surroundings in an appropriate structure for descriptor extraction? In recent years, convolutional neural networks (CNNs) have received much attention for automatic segmentation and classification. Previous studies demonstrated deep learning models' high potential and robust performance for classifying complicated point clouds and permutation invariance. Nevertheless, such algorithms still extract descriptors from independent points without investigating the deep descriptor relationship between the center point and its neighbors. This paper proposes a robust and efficient CNN-based framework named D-Net for automatically classifying a mobile laser scanning (MLS) point cloud in urban areas. Initially, the point cloud is converted into a regular voxelized structure during a preprocessing step. This helps to overcome the challenge of irregularity and inhomogeneity. A density value is assigned to each voxel that describes the point distribution within the voxel's location. Then, by training the designed CNN classifier, each point will receive the label of its corresponding voxel. The performance of the proposed D-Net method was tested using a point cloud dataset in an urban area. Our results demonstrated a relatively high level of performance with an overall accuracy (OA) of about 98% and precision, recall, and F1 scores of over 92%. [ABSTRACT FROM AUTHOR]

The metaverse is an upcoming computing paradigm aiming towards blending reality seamlessly with the artificially generated 3D worlds of deep cyberspace. This giant interactive mesh of three-dimensional reconstructed realms has recently received tremendous attention from both an academic and commercial point of view owing to the curiosity instilled by its vast possible use cases. Every virtual world in the metaverse is controlled and maintained by a virtual service provider (VSP). Interconnected clusters of LiDAR sensors act as a feeder network to these VSPs which then process the data and reconstruct the best quality immersive environment possible. These data can then be leveraged to provide users with highly targeted virtual services by building upon the concept of digital twins (DTs) representing digital analogs of real-world items owned by parties that create and establish the communication channels connecting the DTs to their real-world counterparts. Logically, DTs represent data on servers where postprocessing can be shared easily across VSPs, giving rise to new marketplaces and economic frontiers. This paper presents a dynamic and distributed framework to enable high-quality reconstructions based on incoming data streams from sensors as well as to allow for the optimal allocation of VSPs to users. The optimal synchronization intensity control problem between the available VSPs and the feeder network is modeled using a simultaneous differential game, while the allocation of VSPs to users is modeled using a preference-based game-theoretic approach, where the users give strict preferences over the available VSPs. [ABSTRACT FROM AUTHOR]

Digitalization and sustainability are twin trends in the construction industry (CI). Mobile devices are becoming more and more powerful, gaining the ability to support tasks that, until recently, were unthinkable. One example is LiDAR. The use of this technology embedded in tablets and smartphones brings new possibilities at a scan-to-BIM level. Given the commitment to circularity, waste audits will become mandatory for renovation projects. This is a heavy task that can benefit from existing processes and from digital technologies. This paper aims to demonstrate how everything is related and can be efficiently combined. The research convenes digital transition, green transition, circular economy, waste audit, LiDAR, and design processes' improvement. A mixed-methods approach aims to demonstrate how it is possible to establish digital waste audits and how these can become the core of the diagnosis process to be made during strategic definition or as part of the renovation design's initial actions. LiDAR technology embedded in mobile devices is worthy and can bring major improvements to the diagnosis phase. This will become more compliant with objectives set for digitalization and improved sustainability, contributing to the sector's twin digital and green transitions. [ABSTRACT FROM AUTHOR]

The paper is devoted to the digital transformation of the core engineering maintenance processes involved in medium-voltage overhead distribution network infrastructure inspection. The study presents an analysis of digital twin utilization for inspection of the infrastructure of medium-voltage overhead distribution networks. At present, the infrastructure monitoring process is still manual, and its automatization is a challenging task due to the large distances between and small dimensions of the elements involved. The proposed digital twin is employed for 3D infrastructure modeling and complex analysis based on photogrammetry and aerial scanning data processing methods. The paper describes practical use cases for these data-driven methods for specific infrastructure management processes, e.g., scheduled inspection processes, including geometrical parameter measurements and visual infrastructure element defect identification, unplanned inspection processes (state of emergency, post-storm, etc.), and vegetation management processes. The proposed method allows operations to be performed remotely without physical presence in the field. At the same time, data-driven solutions provide objective results and potential automation via machine learning algorithms, which are more profitable economically. As the proposed method allows processes to be performed remotely, these data-driven solutions offer cost-effective results for automation. The digital twin concept is applied to the Latvian medium-voltage overhead distribution network with the support of the Latvian distribution system operator "Sadales tīkls" JSC. [ABSTRACT FROM AUTHOR]

The development of digital twin for smart city applications requires real-time monitoring and mapping of urban environments. This work develops a framework of real-time urban mapping using an airborne light detection and ranging (LIDAR) agent and game engine. In order to improve the accuracy and efficiency of data acquisition and utilization, the framework is focused on the following aspects: (1) an optimal navigation strategy using Deep Q-Network (DQN) reinforcement learning, (2) multi-streamed game engines employed in visualizing data of urban environment and training the deep-learning-enabled data acquisition platform, (3) dynamic mesh used to formulate and analyze the captured point-cloud, and (4) a quantitative error analysis for points generated with our experimental aerial mapping platform, and an accuracy analysis of post-processing. Experimental results show that the proposed DQN-enabled navigation strategy, rendering algorithm, and post-processing could enable a game engine to efficiently generate a highly accurate digital twin of an urban environment. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) is a leading technology in data acquisition for building information modeling (BIM) applications due to its rapid, direct, and accurate scanning of different objects with high point density. Three-dimensional point cloud classification is essential step for Scan-to-BIM applications that requires high accuracy classification methods, running at reasonable processing time. The classification process is divided into three main steps: neighborhood definition, LiDAR-derived features extraction, and machine learning algorithms being applied to label each LiDAR point. However, the extraction of LiDAR-derived features and training data are time consuming. This research aims to minimize the training data, assess the relevance of sixteen LiDAR-derived geometric features, and select the most contributing features to the classification process. A pointwise classification method based on random forests is applied on the 3D point cloud of a university campus building collected by a TLS system. The results demonstrated that the normalized height feature, which represented the absolute height above ground, was the most significant feature in the classification process with overall accuracy more than 99%. The training data were minimized to about 10% of the whole dataset with achieving the same level of accuracy. The findings of this paper open doors for BIM-related applications such as city digital twins, operation and maintenance of existing structures, and structural health monitoring. [ABSTRACT FROM AUTHOR]

The advancement in sensors, robotics, and artificial intelligence has enabled a series of methods such as simultaneous localization and mapping (SLAM), semantic segmentation, and point cloud registration to help with the reality capture process. To completely investigate an unknown indoor space, obtaining a general spatial comprehension as well as detailed scene reconstruction for a digital twin model requires a deeper insight into the characteristics of different ranging sensors, as well as corresponding techniques to combine data from distinct systems. This paper discusses the necessity and workflow of utilizing two distinct types of scanning sensors, including depth camera and light detection and ranging sensor (LiDAR), paired with a quadrupedal ground robot to obtain spatial data of a large, complex indoor space. A digital twin model was built in real time with two SLAM methods and then consolidated with the geometric feature extraction methods of fast point feature histograms (FPFH) and fast global registration. Finally, the reconstructed scene was streamed to a HoloLens 2 headset to create an illusion of seeing through walls. Results showed that both the depth camera and LiDAR could handle a large space reality capture with both required coverage and fidelity with textural information. As a result, the proposed workflow and analytical pipeline provides a hierarchical data fusion strategy to integrate the advantages of distinct sensing methods and to carry out a complete indoor investigation. It also validates the feasibility of robot-assisted reality capture in larger spaces. [ABSTRACT FROM AUTHOR]

Light detection and ranging technology allows for the creation of detailed 3D point clouds of physical objects and environments. Therefore, it has the potential to provide valuable information for the operation of various kinds of cyber-physical systems that need to be aware of, and interact with, their surroundings, such as autonomous vehicles and robots. Point clouds can also become the basis for the creation of digital representations of different assets and a system's operational environment. This article outlines a system architecture that integrates the geo-spatial context information provided by LiDAR scans with behavioral models of the components of a cyber-physical system to create a digital twin. The clear distinction between behavior and data sets the proposed digital twin architecture apart from existing approaches (that primarily focus on the data aspect), and promotes contextual digital twin generation through executable process models. A vaccine logistics automation use case is detailed to illustrate how information regarding the environment can be used for the operation of an autonomous robot carrying out transport preparation tasks. Besides supporting operation, we propose to combine context data retrieved from the system at different points in the logistics process with information regarding instances of executable behavior models as part of the digital twin architecture. The twin can subsequently be used to facilitate system and process monitoring through relevant stakeholders and structure context data in a user-centric fashion. [ABSTRACT FROM AUTHOR]

This review delves into the integration prospects of Urban Metabolism (UM) with industry 4.0 technologies, particularly digital twin (DT), to bolster sustainable urban development. Embracing a DT framework can enable the visualization of interdependencies within civil systems, facilitating the prediction of interventions to mitigate future climate change impacts. Therefore, the scope of this review was to determine how UM results are represented to end users, such as architects, and engineers and evaluate if these methods align with industry practices. Initially, we analyzed 70 UM studies to investigate the methods and visualization techniques employed. Trends revealed minimal integration of UM with Industry 4.0 technologies or DTs. The findings underscored a lack of dynamic and geographically oriented visualization in UM, with static representation methods like bar charts and Sankey diagrams prevailing, and mostly disconnected from the geographical location of the flows represented. Consequently, the research was further expanded to explicitly review the use of emerging technologies from Industry 4.0, including DTs, using targeted words. An extended review showed a gap in the integration of the UM framework with DT technologies, although an increased use in mapping was depicted. There was inconsistency in the adoption of specific technologies or visualization methods, highlighting ongoing research in this area. The increased use of geographic information system (GIS) and light detection and ranging (LiDAR) marked a pivotal change for more elaborate mapping of UM outcomes. Limited application of dynamic visualizations in UM persists, with MFA diagrams, Sankey diagrams, and bar charts being commonly used methods, despite the need to develop such type of visualizations in the sustainability field. There is an obvious lack of interoperability of these methods with Computer-Aided Design (CAD) software as well as Building Information Model (BIM) software, which are standard tools in urban planning and design. This limitation is particularly significant in environments where the ability to modify planning scenarios and work with precise spatial representations is crucial. We argue that UM can be more effective if integrated with technologies such as DT, which can resolve the spatial issue and describe UM contextualizing it and potentially depicting it in real-time. Additionally, DTs can offer promising solution by integrating UM analysis results and facilitating modifications to the 3-dimensional environment based on this information. This capability empowers planners and designers to conduct simulations, update UM analyses, and iterate the design process, aligning with contemporary practices in planning and design. Given the interdisciplinary nature of UM, which includes various concepts such as urban flows, city flows, system of systems, and urban networks, our study deliberately focuses solely on UM and its associated frameworks. This targeted approach may limit the breadth of concepts explored, but it ensures depth and coherence in our analysis of UM for this study. • Inconsistent methodologies hinder urban metabolism research advancements. • Results highlight the need for improved visualization in urban metabolism. • Results shows need for Industry 4.0 integration in urban metabolism analysis. • Standardization in urban metabolism to foster collaboration and enhance research. • Digital twins' interface may facilitate analysis regardless of technical proficiency. [ABSTRACT FROM AUTHOR]

This Special Issue gathers papers reporting research on various aspects of the use of low-cost photogrammetric and lidar sensors for indoor building reconstruction. It includes contributions presenting improvements in the alignment of mobile mapping systems with and without a prior 3D BIM model, the interpretation of both imagery and lidar data of indoor scenery and finally the reconstruction and enrichment of existing 3D point clouds and meshes with BIM information. Concretely, the publications showcase methods and experiments for the Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling, Two-Step Alignment of Mixed Reality Devices to Existing Building Data, Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption, A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information, 3D Point Cloud Semantic Augmentation for Instance Segmentation of 360° Panoramas by Deep Learning Techniques and the Symmetry-Based Coarse Registration of Smartphone's Colorful Point Clouds with CAD Drawings (RegARD) for Low-Cost Digital Twin Buildings. [ABSTRACT FROM AUTHOR]

The population density in urban areas is rapidly rising, leading to a constant need for new infrastructure and services for citizens. To reduce the time to implementation and optimise the monetary cost of various solutions, the plans and policies of local authorities and stakeholders would benefit from undergoing a series of virtual stress tests. To this end, prescriptive and predictive technologies are widely adopted to optimise city planning and to understand the urban processes and environment such as air pollution and transportation. Nevertheless, holistic sandboxes tightly integrated with cities are still largely lacking. The city digital twin is a promising concept that provides a tool for exploration of new solutions in a controlled environment before their deployment. The digital twin is a virtual replica of the real city, which collects data from the infrastructure, processes and services using not only the available systems, but also purposely built connected devices and sensors. In this context, the establishment of urban living labs facilitates the monitoring and understanding of urban processes and enriches the digital twin with highly-relevant data. This paper presents an urban living lab, under deployment in the district of Lozenets in Sofia, Bulgaria. It is part of a larger initiative for developing a city digital twin of Sofia to support the design, exploration, and experimentation of different solutions. The living lab is equipped with sensors for monitoring air quality, atmospheric parameters, noise pollution and pedestrian flows. In addition, a Light Detection and Ranging (LiDAR) system is realised as an edge computing facility at one of the busiest intersections of the district. Along with the equipment, the paper describes the architecture and components of the platform for data collection, storage, processing, and visualization. Finally, high-priority studies are presented, and their demographic and economic impact is discussed. [ABSTRACT FROM AUTHOR]

The metaverse is an upcoming computing paradigm aiming towards blending reality seamlessly with the artificially generated 3D worlds of deep cyberspace. This giant interactive mesh of three-dimensional reconstructed realms has recently received tremendous attention from both an academic and commercial point of view owing to the curiosity instilled by its vast possible use cases. Every virtual world in the metaverse is controlled and maintained by a virtual service provider (VSP). Interconnected clusters of LiDAR sensors act as a feeder network to these VSPs which then process the data and reconstruct the best quality immersive environment possible. These data can then be leveraged to provide users with highly targeted virtual services by building upon the concept of digital twins (DTs) representing digital analogs of real-world items owned by parties that create and establish the communication channels connecting the DTs to their real-world counterparts. Logically, DTs represent data on servers where postprocessing can be shared easily across VSPs, giving rise to new marketplaces and economic frontiers. This paper presents a dynamic and distributed framework to enable high-quality reconstructions based on incoming data streams from sensors as well as to allow for the optimal allocation of VSPs to users. The optimal synchronization intensity control problem between the available VSPs and the feeder network is modeled using a simultaneous differential game, while the allocation of VSPs to users is modeled using a preference-based game-theoretic approach, where the users give strict preferences over the available VSPs.

The new dual-channel airborne LiDAR system can acquire dense point clouds of roofs and facades at the same time, RIEGL VQ-1560i has the most advanced dual-channel LiDAR system with unique and innovative bi-directional scanning angles, which provides better building instance extraction than traditional Scanner for more complete and precise data. This abstract presents the first point cloud building extraction for urban digital twins using dual-channel airborne LiDAR data. The main challenges of this lidar data are the large number of points, complex data structure, and multiple classes of objects. We propose a preprocessing-free architectural extraction method. It consists of three steps, namely point cloud slicing, projection, and constraint-based extraction of labels. Point cloud slices consist of top-down merging of elevation and 3D semantic segmentation to reorganize point cloud scenes into interrelated point groups. This greatly reduces the processing difficulty and computational burden of complex structures while removing multiple classes of non-building points. Second, we project the point group into an image to further reduce computational complexity while improving processing efficiency. Finally, we label the building with its up-down relationship and remap it as a 3D building. Experimental results show that the proposed method achieves an average recall rate of 95.36% and an average F1 score of 93.59%. For digital twin instance segmentation, the quality of the two public test scenarios reaches 92.86% and 98.31%, respectively. [ABSTRACT FROM AUTHOR]

Point clouds are widely used for structure inspection and can provide damage spatial information. However, how to update a digital twin (DT) with local damage based on point clouds has not been sufficiently studied. This research presents an efficient framework for assessing and DT synchronizing local damage on a planar surface using point clouds. The pipeline starts from damage detection via DeepLabV3+ on the pseudo grayscale images from the point depth. It avoids the drawbacks of image and point cloud fusion. The target point cloud is separated according to the detected damage. Then, it can be converted into a 3D binary matrix through voxelization and binarization, which is highly lightweight and can be losslessly compressed for DT synchronization. The framework is validated via two case studies, demonstrating that the proposed voxel-based method can be easily applied to real-world damage with non-convex geometry instead of convex-hull fitting; finite-element (FE) models and BIM models can be updated automatically through the framework. • This study proposes an efficient framework for assessing and DT synchronizing 3D local damage using point clouds, which involves surface damage detection, volumetric damage segmentation, and model updating. Damage segmentation is achieved with deep learning (DL) and morphological operations. The separated point cloud can be converted into a highly lightweight 3D binary matrix and further losslessly compressed for data transmission. • Surface damage detection is achieved through a DeepLabV3+ model on pseudo grayscale images from the point depth, avoiding extra fusion of image and point cloud. The approach is validated on specimens with variant crack widths and depths, which demonstrates that the DL model trained from real grayscale images can be applied to pseudo grayscale images for damage detection when the damage satisfies appropriate conditions. • The proposed voxel-based fitting method for damage volumetric assessment, which does not require elaborate manual cutting for each component, can be easily applied to 3D damage with complicated non-convex geometry instead of the convex-hull fitting in previous research. • FE and BIM geometric models can be updated automatically with the local damage based on the transmitted 3D binary matrix. [ABSTRACT FROM AUTHOR]

In this work, the steps of the development of a reduced complexity model (RCM) of a light detection and ranging (LiDAR) system and the requirements for the preparation of a digital twin (DT) from such system are discussed. Preliminary thermal and optical simulations are presented, along with different concepts for cooling of the system. Additionally, current barriers of the direct coupling of thermo-mechanical and optical simulations performed in Ansys Mechanical and Zemax OpticStudio, respectively are discussed. • Effect of the thermomechanical loads on the optical system • Preparation steps for a Digital Twin • Structural thermal optical performance analysis (STOP) • Time of flight (ToF) simulation • Board level cooling mechanism [ABSTRACT FROM AUTHOR]

Climate change adaptation, natural and man-made disasters, soil sealing, biodiversity loss, equal living conditions and demographic change are only some of the challenges that current and future government authorities face. They are characterised by their vast geographic scope, high complexity and interdependence. To address these issues in a modern, data-driven way, new tools are needed to support policy makers in finding reliable, efficient solutions. The German Federal Agency for Cartography and Geodesy (BKG) responds to these requirements with the inception of a geospatial digital twin that will allow government authorities to model, simulate and test the impacts of possible solutions and gain new insights into underlying effects. Digital twins have long been used in industrial production to plan and monitor processes in a holistic way. The idea is that a digital replica of an object or process is used for testing and simulation without the risk and cost of trying it in the real world. A digital twin is bidirectionally linked to its real-world counterpart. This means that it is informed by the real world (such as sensors) and at the same time allows deductions about the real world in return (i.e. expected failure date of a part). Likewise, it should be imaginable to adapt this approach to a much wider scale, such as cities, an entire country or the whole world. Each level of scale levels presents its own challenges, but also bears tremendous potential which should not go unexplored. In geoinformatics the term digital twin has not yet been conclusively defined. For BKG however, one key aspect is interoperability which promotes data integration from different subject areas and elevates insights through better context. To achieve this, Digital Twin Germany will incorporate as much authoritative data as possible from existing information systems and infrastructures of federal authorities. A second keystone of a geospatial digital twin is a standard inventory of assets, such as buildings, topography or public infrastructure. BKG will capture the entirety of Germany within one year using high resolution airborne LiDAR. This unprecedented scope is now achievable due to recent advances in LiDAR technology, such as Geiger-mode LiDAR (GmLiDAR) or Single Photon LiDAR (SPL). The result is a unique, high-resolution dataset with at least 40 points per square metre that is characterised by its homogeneity. The entire country is captured in a temporally and methodically consistent way. Another central characteristic of a digital twin is that it is a dynamic construct that should allow for connections between the digital and the real world. This is achieved through the integration of sensors such as Internet-of-Things (IoT) or earth observation (EO) data captured continuously by satellites. The latter is supplied as analysis ready data (ARD) through a federated structure, building on existing solutions in the domain. BKG aims to provide access to its users to all the data through a single platform, including processing capabilities. This follows the growing requirements of its customers in government administration to fully leverage spatial analytics on a country-wide scale. Similarly, this follows the recent paradigm shift in the geospatial sector to bring users -- and processing -- to the data instead of transferring the data to the user. The platform will enable users of the federal administration to integrate their own data and ideas or contract BKG to implement it. On this platform, users can continuously model, simulate and monitor their use cases to gain new insights. They will be able to virtually test and compare different decision alternatives without the risks and cost of trying them in the real world first. Digital twins are, at their core, about information exchange: sharing between real and digital worlds, methods and results, and between applications. The homogeneity of the data captured from the 3D survey builds a common ground to facilitate this within Digital Twin Germany. There are several other digital twin initiatives in the geospatial sector, such as Destination Earth (DestinE) on a global scale or Connected Urban Twins (CUT) on a city-level scale. To succeed, each of these projects must consider integration and continuance beyond their own particular scope. BKGs Digital Twin Germany positions itself firmly in between, on a national level, and foresees interfaces for collaboration. In preparation for Digital Twin Germany, a demonstration project was carried out in the Hamburg Metropolitan Region. BKG tested the feasibility of a large-scale high-resolution data capture using the SPL100 sensor over an area of 8650km². The resulting dataset has a vertical accuracy of less than 10cm. The project team gained a significant understanding of the challenges and opportunities of this data. Likewise, a network was established with potential users to formally assess their needs and expectations and to foster collaboration on a national level. BKG will share preliminary findings and the challenges encountered, as well as their vision for the concept behind Digital Twin Germany. [ABSTRACT FROM AUTHOR]

• A new procedure for assessing intervisibility between conflicting agents (IvCA) • An agent-based approach for identifying conflicting agents at intersections in real-time. • Both static infrastructure and dynamic agents were modeled as sight obstacles. • Assessing IvCA at each frame within 0.1 s on average with limited computational power. A new procedure is developed for effectively monitoring intervisibility between conflicting agents in real-time at intersections. Dense light detection and ranging (Lidar) point clouds and time-stamped trajectory data are used to model static intersection environment and emulate traffic participants' dynamic motion states, respectively. The proposed procedure reads trajectory data in sequence according to their timestamps. An agent-based approach that enables the application of multi-core parallel computing is applied to estimate conflict points and identify conflicting agents in pairs. Then, a linear elevation array is created from the Lidar point cloud data, based on which the elevation of each trajectory point is obtained in a real-time manner. Meanwhile, three-dimensional bounding cuboids are generated at each path point to represent digital twins of agents. Once a pair of conflicting agents are identified, a hybrid approach is triggered to examine whether the agents' line-of-sights are occluded by either static (e.g., tree trunk) or dynamic obstacles (e.g., cars). Accordingly, virtual warning signals can be generated. The effectiveness of the procedure is demonstrated through controlled experiments. The procedure was also tested in two virtual scenarios. The mean processing time at each frame is less than 0.1 s as achieved with limited computational power. With implementation of parallel computing technique, processing time is not sensitive to number of agents within the intersection. In addition, by enabling the outputs of virtual warning signals, spatial distribution of conflict points, and individual conflict-related time series data, the procedure shall help provide substantial insights into intersection safety. [ABSTRACT FROM AUTHOR]

Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed. [ABSTRACT FROM AUTHOR]