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This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling the loads of air-conditioning systems in summer, and the urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D building data. It aims to explore the methods of geometry optimisation, limitations, and performance issues of a 3D computer-aided design (CAD) tool dedicated to small-scale solar analysis and employed at the city scale. Two cases were considered at the city and district scales, respectively. The total face count of meshes for the simulations constituted approximately 2,000,000 faces. A total of 64,379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualised in a 3D web platform. The use of a 3D CAD environment establishes a seamless process of updating 3D models and simulations, while preprocessing in Geographic Information System (GIS) ensures working with large-scale datasets. The proposed method showed a moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future. [ABSTRACT FROM AUTHOR]

A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field. [ABSTRACT FROM AUTHOR]

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges. [ABSTRACT FROM AUTHOR]

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified. [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 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]

Digital Twin (DT) plays a crucial role in intelligent bridge management, and the geometric DT (gDT) serves as its foundation. Notably, the fast and high-precision generation of bridge gDT models has gained increasing attention. This research presents a method for generating high-precision and fast RC bridges with chambers for gDT using terrestrial laser scanning. The method begins with a proposed fast point cloud data collection technique designed specifically for bridges with internal chambers. Subsequently, Euclidean clustering and grid segmentation algorithms are developed to automatically extract contour features from the sliced point clouds. Finally, a framework based on the Dynamo–Revit reverse modelling method is introduced, enabling the automatic generation of gDT models from the identified point cloud features. To validate the feasibility and accuracy of the proposed method, a concrete variable section bridge is used. A comparison is made between the generated gDT model and the point cloud model in terms of 3D deviation, revealing a maximum deviation of 6.6 mm and an average deviation of 3 mm. These results affirm the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The effectiveness indicator system of remote sensing satellites includes various satellites capabilities. Effectiveness evaluation is the process of calculating these indicators in the digital world, involving many different physical parameters of multiple subsystems. Model-based simulation statistics method is the mainstream approach of effectiveness evaluation, and digital twin is currently the most advanced modeling method for simulation. The satellite digital twin model has the characteristics of multi-dynamic, multi-spatial scale and multi-physics field coupling, which gives rise to challenges related to the stiff problem of ordinary differential equations and multi-scale problem of partial differential equations to the calculation process of indicators. It is difficult to solve these problems by breakthroughs in numerical solution methods. This paper uses the sparsity of the satellite system to group each indicator of the effectiveness evaluation indicator system according to the change period. The satellite system model is decomposed into multiple modules according to the composition and structure, and a series of models with different simulation fidelity are established for each module. The optimization schemes for selecting model granularity when calculating indicators by group is given. Simulation results show that this approach considers the coupling between systems, grasps the main contradiction of indicator calculation and overcomes the loss of indicator accuracy caused by the separate calculation of each subsystem under the neglect of coupling in the traditional method. Additionally, it avoids the difficulty in numerical calculation caused by coupling, while simultaneously balancing the accuracy and efficiency of the model simulations. [ABSTRACT FROM AUTHOR]

Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient integrated systems that rapidly communicate intelligent self-adaptation for collaborative operations. Here, we propose a critical futuristic integrated communication element named City Sensing Base Station (CSBS), inspired by base stations for cell phones that address similar concerns. A CSBS is designed to handle massive volumes of heterogeneous observation data that currently need to be upgraded by middleware or registered. It also provides predictive and interpolation modelling for the control of sensors and response units such as emergency services and drones. A prototype of CSBS demonstrated that it could unify readily available heterogeneous sensing devices, including surveillance video, unmanned aerial vehicles, and ground sensor webs. Collaborative observation capability was also realized by integrating different object detection sources using advanced computer-vision technologies. Experiments with a traffic accident and water pipeline emergency showed sensing and intelligent analyses were greatly improved. CSBS also significantly reduced redundant Internet connections while maintaining high efficiency. This innovation successfully integrates high-density, high-diversity, and high-precision sensing in a distributed way for the future digital twin of cities. [ABSTRACT FROM AUTHOR]

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified.

The Destination Earth (DestinE) European initiative has recently brought into the scientific community the concept of the Digital Twin (DT) applied to Earth Sciences. Within 2030, a very high precision digital model of the Earth, continuously fed and powered by Earth Observation (EO) data, will provide as many digital replicas (DTs) as the different domains of the earth sciences are. Considering that a DT is driven by use cases, depending on the selected application, the provided information has to change. It follows that, to achieve a reliable representation of the selected use case, a reasonable and complete a priori definition of the needed elements that DT must contain is mandatory. In this work, we define a possible theoretical framework for a future DT of the Italian Alpine glaciers, trying to define and describe all those information (both EO and in situ data) and relationships that necessarily have to enter the process as building blocks of the DT itself. Two main aspects of glaciers were considered and investigated: (i) the "metric quantification" of their spatial dynamics (achieved through measures) and (ii) the "qualitative (semantic) description" of their health status as definable through observations from domain experts. After the first identification of the building blocks, the work proceeds focusing on existing EO data sources providing their essential elements, with specific focus on open access high-resolution (HR) and very-high-resolution (VHR) images. This last issue considered two scales of analysis: local (single glacier) and regional (Italian Alps). Some considerations were furtherly reported about the expected glaciers-related applications enabled by the availability of a DT at regional level. Applications involving both metric and semantic information were considered and grouped in three main clusters: Glaciers Evolution Modelling (GEM), 4D Multi Reality, and Virtual Reality. Limitations were additionally explored, mainly related to the technical characteristics of available EO VHR open data and some conclusions provided. [ABSTRACT FROM AUTHOR]

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]

This paper presents the results of a multiyear program conducted in Khiva, by a research team from the University of California, Berkeley (USA) and Urgench State University, Urgench (Uzbekistan). It was focused on the Juma (Djuma) Mosque in Itchan Kala (Khiva, Uzbekistan). The main objective was to generate a digital twin of the mosque, with an accuracy of a few millimeters, by utilizing a laser scanner. The idea of a digital twin was expanded further, to ensure that the physical properties and structural response of the digital twin were closely correlated to that of the actual object. To achieve this objective, the following was conducted. First, a laser scanning of the historic monument was conducted. The laser scans were collected by a terrestrial laser scanner. Subsequently, a study of the monument's structural response was conducted in ambient vibration tests that focused on measuring the resonant frequencies of the mosque's minaret. Based on all of the information collected during both field studies, a sophisticated finite element model of the minaret was developed. The calibration of the model was based on the results of the ambient vibration study. The performance of the model was shown to be close to that of the actual monument. The digital twin and associated numerical model will be used in structural health monitoring, numerical predictions of the structural performance, and in the development of restoration strategies. [ABSTRACT FROM AUTHOR]

Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display methods were either charts or two-dimensional map-based visualization methods. Breaking through the traditional visualization methods of environmental sensing data and realizing a multi-dimensional and multi-view display of environmental sensing data in a digital twin of lakes is something that this particular paper tries to resolve. This study proposes a visualization framework to integrate, manage, analyze, and visualize the environmental sensing data in the digital twin of lakes. In addition, this study also seeks to realize the coupling expression of geospatial data and long-term monitoring sequence data. Different visualization methods are used to realize the visualization of environmental sensing data in the digital twin of lakes. Using vector and scalar visualization methods to display ambient wireless sensor monitoring data in a digital twin of lakes provides researchers with richer visualization methods and means for deeper analysis. Using video fusion technology to display environmental sensing video surveillance data strengthens the integration of the virtual environment and real space and saves time for position identification using video surveillance. These findings may also help realize the integration and management of real-time environmental sensing data in a digital twin of lakes. The visualization framework uses various visualization methods to express the monitoring data of environmental wireless sensors and increases the means of visualizing environmental sensing data in the world of a lake digital twin. This visualization framework is also a general approach that can be applied to all similar lakes, or other geographical scenarios where environmental sensing devices are deployed. The establishment of a digital twin of Poyang Lake has certain practical significance for improving the digital management level of Poyang Lake and monitoring its ecological changes. Poyang Lake is used as an example to verify the proposed framework and method, which shows that the framework can be applied to the construction of a lake-oriented digital twin. [ABSTRACT FROM AUTHOR]

The idea of a walkable city refers to the extent to which the built environment encourages people to walk by establishing comfortable pedestrian routes, which allows people to connect to numerous services with reasonable effort and time. Walkability is currently regarded as a "good to know about" rather than a "must-have" factor for sustainable development. A combination of walkability with a standard design strategy, such as generative design, may result in a more efficient way of planning a walkable city. Interestingly, the sole indicator taken into account for walkability in the generative design domain is "distance to amenities", while in reality, other parameters, such as the comfort factor, could also influence walkability. Therefore, in this research, we developed a workflow based on the generative design, which considers the comfort dimension in combination with distance to amenities and street-level greeneries. We also included the human perspective, given that walkability is always personal. This research successfully generated three different scenarios of walkability-optimal urban plans, where the highest walkability is 82.43 (very walkable). Furthermore, the baseline scenario of two different locations also aligns with people's perspectives when compared. In addition, we found that the inclusion of a temporal dimension, enhanced 3D-related indicators, and constraints should benefit future research. [ABSTRACT FROM AUTHOR]

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks. [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]

Digital twins of forests (trees) are computational virtual recreations of forests (trees) in which the entity distributions and physical processes in real-world forests (trees) are duplicated. It is expected that conventional forest science and management can be undertaken in a digital twin of forests (trees) if the recreation of a real-world forest (tree) has accurate and comprehensive enough information. However, due to the various differences between the current tree model and the real tree, these envisioned digital twins of the forests (trees) stay a theoretical concept. In this study, we developed a processing strategy that partially integrated computational virtual measurement (CVM) process into the tree modeling workflow. Owing to the feature of CVM, partial tree skeleton reconstruction procedures were considered to have higher mechanical objectivity compared to conventional mathematical modeling methods. The reason was that we developed a novel method called virtual diameter tape (VDT), which could provide a certain percentage of modeling elements using CVM. Technically, VDT was able to virtually measure diameters and spatial distribution of cross-sectional area of trees, including the basal area, from point clouds. VDT simulated the physical scenario of diameter tapes, observing point clouds of trees. Diameter and the cross-sectional area of stem and branches were obtained by two consecutive physical measurement processes, one in the forest sample site and another in the virtual space. At the same time, VDT obtained better or a similar accuracy compared to the mathematical methods, i.e., Hough transform-based methods, using the same data sets. The root-mean-square deviation (RMSE) of retrieval of diameter at breast height (DBH) using VDT was 1.02 cm, while DBH obtained from three conventional methods varied from 1.29 cm to 1.73 cm. Based on VDT measurement results, tree skeleton reconstruction and actual forest scenario rendering of our sample plots were further implemented. Beyond the visual consistency, we believe that our work might be a small and solid step in the technological evolution from tree models to the digital twin of forests (trees). [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]

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]

Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach. [ABSTRACT FROM AUTHOR]

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions. [ABSTRACT FROM AUTHOR]

The construction of digital twin cities is a current research hotspot. Video data are one of the important aspects of digital twin cities, and their digital modeling is one of the important foundations of its construction. For this reason, the construction and digital analysis of video data space has become an urgent problem to be solved. After in-depth research, this study found that the existing video space construction methods have three shortcomings: first, the problem of high requirements for objective conditions or low accuracy; second, the lack of easy and efficient mapping algorithms from 2D video pixel coordinates to 3D; and third, the lack of efficient correlation mechanisms between video space and external geographic information, making it difficult to integrate video space with external information, and thus prevent a more effective analysis. In view of the above problems, this paper proposes a video stereo grid geometric space construction method based on GeoSOT-3D stereo grid coding and a camera imaging model to form a video stereo grid space model. Finally, targeted experiments of video stereo grid space geometry construction were conducted to analyze the experimental results before and after optimization and compare the variance size to verify the feasibility and effectiveness of the model. [ABSTRACT FROM AUTHOR]

In the context of digital twins, smart city construction and artificial intelligence technology are developing rapidly, and more and more mobile robots are performing tasks in complex and time-varying indoor environments, making, at present, the unification of modeling, dynamic expression, visualization of operation, and wide application between robots and indoor environments a pressing problem to be solved. This paper presents an in-depth study on this issue and summarizes three major types of methods: geometric modeling, topological modeling, and raster modeling, and points out the advantages and disadvantages of these three types of methods. Therefore, in view of the current pain points of robots and complex time-varying indoor environments, this paper proposes an indoor spacetime grid model based on the three-dimensional division framework of the Earth space and innovatively integrates time division on the basis of space division. On the basis of the model, a dynamic path planning algorithm for the robot in the complex time-varying indoor environment is designed, that is, the Spacetime-A* algorithm (STA* for short). Finally, the indoor spacetime grid modeling experiment is carried out with real data, which verifies the feasibility and correctness of the spacetime relationship calculation algorithm encoded by the indoor spacetime grid model. Then, experiments are carried out on the multi-group path planning algorithms of the robot under the spacetime grid, and the feasibility of the STA* algorithm under the indoor spacetime grid and the superiority of the spacetime grid are verified. [ABSTRACT FROM AUTHOR]

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

The construction of digital twin cities is a current research hotspot. Video data are one of the important aspects of digital twin cities, and their digital modeling is one of the important foundations of its construction. For this reason, the construction and digital analysis of video data space has become an urgent problem to be solved. After in-depth research, this study found that the existing video space construction methods have three shortcomings: first, the problem of high requirements for objective conditions or low accuracy; second, the lack of easy and efficient mapping algorithms from 2D video pixel coordinates to 3D; and third, the lack of efficient correlation mechanisms between video space and external geographic information, making it difficult to integrate video space with external information, and thus prevent a more effective analysis. In view of the above problems, this paper proposes a video stereo grid geometric space construction method based on GeoSOT-3D stereo grid coding and a camera imaging model to form a video stereo grid space model. Finally, targeted experiments of video stereo grid space geometry construction were conducted to analyze the experimental results before and after optimization and compare the variance size to verify the feasibility and effectiveness of the model.

This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation.

This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the "Digital Twins of the Earth" may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of "Digital Twins of the Earth". The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation. [ABSTRACT FROM AUTHOR]