Search Results (143)

Effective water management is crucial in urban and rural settings, requiring efficient usage and timely detection of issues like leakages for sustainability. This paper introduces an integrated framework that combines Digital Twin technology with a multimodal transformer-based model for accurate water usage prediction and leakage detection. The system synchronizes real-time data from various sensors including flow meters, pressure sensors, and thermal imaging devices with a Digital Twin of the water network. Advanced transformer models, specifically the Informer model for long-term time-series prediction and a Water Multimodal Transformer for anomaly detection, process these data to capture complex patterns and dependencies. Experimental results demonstrate the framework’s effectiveness: the Informer model achieved an R² score of 0.9995 and a Mean Squared Error (MSE) of 2.2, outperforming traditional models. For leakage detection, the model attained 98.4% accuracy and precision, an F1 score of 97.6%, a low False Positive Rate of 0.0019, and an Area Under the Curve (AUC) of 0.984. By fusing diverse sensor data and utilizing advanced transformer architectures, the framework provides a comprehensive view of the water network, enabling real-time decision-making, enhancing forecasting accuracy, and reducing water waste. This scalable solution supports sustainable water management practices in both urban and industrial contexts. Full article

Due to the increasing number of vehicles and the limited land supply, old residential areas generally face parking difficulties. An intelligent parking service is a critical study direction to address parking difficulty since it can achieve the automatic management of parking processes and planning of parking spaces. However, the existing intelligent parking service systems have shortcomings such as low information quality, low management efficiency, and single service mode. To address the shortcomings, in this paper, we conduct a systematic study on utilizing digital twin (DT) technology to improve the intelligent parking service system. The main contributions are threefold: (1) We analyze the function requirements of the intelligent parking service for old residential areas, such as visual monitoring, refined management, and simulation optimization. (2) We design a DT-based intelligent parking service system by collecting data on physical parking space, constructing the corresponding virtual parking space, and building the user interaction platform. An old residential area in Guangzhou, China is used as a use case to show that the designed parking service system can meet the function requirements. (3) Through mathematical modeling and simulation evaluation, we utilize two typical intelligent parking services including dynamic parking planning and driving safety assessment to demonstrate the effectiveness of the proposed system. This study provides innovative solutions for parking management in old residential areas, utilizing DT technology to not only improve information quality and management efficiency, but also provide a theoretical basis and practical reference for the intelligent transformation of urban parking services. Full article

This paper presents COWIN^E (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWIN^E aims to enhance the disaster resilience of communities by fostering collaborative participation in the use of its DT among the decision-makers, the general public, and other involved stakeholders. COWIN^E leverages Cities:Skylines as its base simulation engine integrated with real-world data for community DT development. It is capable of capturing the dynamic, intricate, and interconnected structures of communities to provide actionable insights into disaster resilience planning. Through demonstrative, simulation-based case studies on Brevard County, Florida, the paper illustrates COWIN^E’s collaborative use with the involved parties in managing tornado scenarios. This study demonstrates how COWIN^E supports the identification of vulnerable areas, the execution of adaptive strategies, and the efficient allocation of resources before, during, and after a disaster. This paper further explores potential research directions using COWIN^E. The findings show COWIN^E’s potential to be utilized as a collaborative tool for community disaster resilience management. Full article

Citizen participation is key in urban planning, but traditional methods are often limited in terms of accessibility and inclusion. This study investigates how the use of emerging technologies such as Virtual and Augmented Reality (VR/AR), Digital Twin (DT), Building Information Modelling (BIM), Artificial Intelligence (AI), and Geographic Information Systems (GIS) can enhance citizen participation in urban planning. Through the review and analysis of existing literature, combined with the study of cases from cities in Eurasia and North America on the implementation of these technologies in urban and environmental planning, the results indicate that the use of multi-reality technologies facilitates immersive visualization of urban projects, allowing citizens to better understand the implications of proposed changes. Furthermore, the integration of real-time monitoring, such as forest and climate surveillance, improves environmental control. Technologies like AI and GIS also enable greater precision and empowerment in participatory decision-making. Nevertheless, the emergence of these technologies presents a challenge that must be addressed, as it is essential to establish a regulatory framework to ensure their responsible use. In conclusion, these platforms not only increase participation and co-creation but also enable more efficient, sustainable, and inclusive urban planning. Greater adoption of these technologies is suggested to optimize the urban decision-making process. Full article

This paper presents a comprehensive review of the transformative impacts of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DTs), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM) and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, designers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development. Full article

Background: Urban mobility management faces growing challenges that require the analysis and optimization of sustainable solutions. Digital twins (DTs) have emerged as innovative tools for this assessment, but their implementation requires standardized procedures and languages; Methods: As part of a broader methodology for continuous DT validation, this study focuses on the conceptual validation phase, presenting a conceptualization approach through formalization using Specification and Description Language (SDL), agnostic to simulation tools. The conceptual validation was achieved through stakeholder engagement in the Bolzano context, producing 41 SDL diagrams that define both elements common to different urban realities and specific local data collection procedures; Results: The feasibility of implementing this stakeholder-validated conceptualization was demonstrated using Simulation of Urban MObility (SUMO) for traffic simulation and optimization criteria calculation, and its framework SUMO Activity GenerAtion (SAGA) for generating an Activity-Based Modeling (ABM) mobility demand that can be improved through real sensor data; Conclusions: The SDL approach, through its graphical representation (SDL/GR), enables conceptual validation by enhancing stakeholder communication while defining a framework that, while adapting to the monitoring specificities of different urban realities, maintains a common and rigorous structure, independent of the chosen implementation tools and programming languages. Full article

Digital Twin (DT) technologies have demonstrated a positive impact across various stages of the Architecture, Engineering, and Construction (AEC) industry. Nevertheless, the industry has been slow to undergo digital transformation. The paper utilizes the Systematic Literature Review (SLR) approach to study a total of 842 papers on the application of DT in buildings, landscapes, and urban environments (BLU) from 2018 to 2024. Based on the research results, suggestions have been made for future research and practical directions. Meanwhile, it provides assistance to BLU’s designers, constructors, managers, and policymakers in establishing their understanding of the digital transformation of the AEC industry. The existing relevant research can be mainly divided into three categories: case study, framework study, and technology study. Compared with the buildings and urban environment industries, the number and depth of research in the landscape industry are relatively low. Through in-depth analysis of BLU projects, three research trends in the future are determined: (1) research and application of DT framework in the design and planning stage; (2) development of design tools and basic theory based on DT model; (3) application and exploration of DT technology in the landscape industry. Full article

SegContrast first paved the way for contrastive learning on outdoor point clouds. Its original formulation targeted individual scans in applications like autonomous driving and object detection. However, mobile mapping purposes such as digital twin cities and urban planning require large-scale dense datasets to capture the full complexity and diversity present in outdoor environments. In this paper, the SegContrast method is revisited and adapted to overcome its limitations associated with mobile mapping datasets, namely the scarcity of contrastive pairs and memory constraints. To overcome the scarcity of contrastive pairs, we propose the merging of heterogeneous datasets. However, this merging is not a straightforward procedure due to the variety of size and number of points in the point clouds of these datasets. Therefore, a data augmentation approach is designed to create a vast number of segments while optimizing the size of the point cloud samples to the allocated memory. This methodology, called CLOUDSPAM, guarantees the performance of the self-supervised model for both small- and large-scale mobile mapping point clouds. Overall, the results demonstrate the benefits of utilizing datasets with a wide range of densities and class diversity. CLOUDSPAM matched the state of the art on the KITTI-360 dataset, with a 63.6% mIoU, and came in second place on the Toronto-3D dataset. Finally, CLOUDSPAM achieved competitive results against its fully supervised counterpart with only 10% of labeled data. Full article

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field. Full article

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process. Full article

In the context of urban sustainability and the development of resilient cities, the use of 4D geospatial data and the integration and association of building information with geographical information are of considerable interest. Achieving this integration is particularly significant in the scientific field from a technical standpoint but poses significant challenges due to the incompatibility between the two environments. This research proposes various methodologies for the effective integration of BIM/GIS data by analyzing their pros and cons and highlights the innovative aspects of the integration between these systems. Starting with the use of commercial software that has enabled the integration of a building’s 3D model within a GIS environment (this system is particularly useful for its ease of management and the potential for practical applications), this study progresses to an experimental virtual/augmented/mixed reality app developed by the authors that allows for the virtual integration of a building with its territorial context. It concludes with an innovative methodology that, by using the customizable and extensible libraries of the Cesium platform, facilitates the integration of structural data within a 4D geospatial space. This study demonstrates the feasibility of integrating BIM and GIS data despite inherent incompatibilities. The innovative use of Cesium platform libraries further enhances this integration, providing a comprehensive solution for intelligent and sustainable urban planning. By addressing the challenges of incompatibility, the final solution offers critical insights for a deeper understanding of evolving urban landscapes and for monitoring urban expansion and its environmental impacts. Full article

This review paper explores Urban Digital Twins (UDTs) and their crucial role in developing smarter cities, focusing on making urban areas more sustainable and well-planned. The methodology adopted an extensive literature review across multiple academic databases related to UDTs in smart cities, sustainability, and urban environments, conducted by a bibliometric analysis using VOSviewer to identify key research trends and qualitative analysis through thematic categorization. This paper shows how UDTs can significantly change how cities are managed and planned by examining examples from cities like Singapore and Dubai. This study points out the main hurdles like gathering data, connecting systems, handling vast amounts of information, and making different technologies work together. It also sheds light on what is missing in current research, such as the need for solid rules for using UDTs effectively, better cooperation between various city systems, and a deeper look into how UDTs affect society. To address research gaps, this study highlights the necessity of interdisciplinary collaboration. It also calls for establishing comprehensive models, universal standards, and comparative studies among traditional and UDT methods. Finally, it encourages industry, policymakers, and academics to join forces in realizing sustainable, smart cities. Full article

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated. Full article

To address the growing need for advanced tools that enable urban policymakers to conduct comprehensive cost-benefit analyses of traffic management changes, the Urbanite H2020 project has developed innovative artificial intelligence methods. Among them is a robust decision support system that assists policymakers in evaluating and selecting optimal urban mobility planning modifications by combining objective and subjective criteria. Utilising open-source microscopic traffic simulation tools, accurate digital models (or “digital twins”) of four pilot cities—Bilbao, Amsterdam, Helsinki, and Messina—were created, each addressing unique mobility challenges. These challenges include reducing private vehicle access in Bilbao’s city center, analysing the impact of increased bicycle traffic and population growth in Amsterdam, constructing a mobility-enhancing tunnel in Helsinki, and improving public transport connectivity in Messina. The research introduces five key innovations: the application of a consistent open-source simulation platform across diverse urban environments, addressing integration and consistency challenges; the pioneering use of Dexi for advanced decision support in smart cities; the implementation of advanced visualisations; and the integration of the machine learning tool, Orange, with a user-friendly GUI interface. These innovations collectively make complex data analysis accessible to non-technical users. By applying multi-label machine learning techniques, the decision-making process is accelerated by three orders of magnitude, significantly enhancing urban planning efficiency. The Urbanite project’s findings offer valuable insights into both anticipated and unexpected outcomes of mobility interventions, presenting a scalable, open-source AI-based framework for urban decision-makers worldwide. Full article

Timeliness, budget consciousness, and quality are critical to the success of a project, and become increasingly challenging with increased project complexity. Five-dimensional building information modeling (BIM) integrates cost and schedule data with a 3D model, and enhances project management by addressing budgeting, timelines, and visualization simultaneously. However, a comprehensive assessment of 5D BIM’s impact on key performance indicators is currently lacking. This research aims to identify the critical factors influencing the adoption of 5D BIM and its impact on key project performance indicators. A thorough systematic literature review and qualitative analysis were conducted to achieve this goal. Relevant articles from the past decade (2014–2023) were examined from the Scopus and Web of Science databases, of which 222 were selected and screened using PRISMA procedures. This research found consistent and rapid updating of keywords, highlighting the dynamic evolution of 5D BIM and its expanding applications in the construction industry. Thirty critical factors affecting the adoption of 5D BIM were identified and categorized into the following six groups based on the technology–organization–environment (TOE) framework: technology, organization, environment, operator, project, and government policy. The 15 factors driving construction project performance in integrated 5D BIM were divided into cost, time, and quality performance based on key performance indicators. This review offers innovative insights into 5D BIM adoption, and can aid stakeholders in developing effective 5D BIM implementations. Full article