Table of Links Abstract and 1 Introduction 1.1. Spatial Digital Twins (SDTs) 1.2. Applications 1.3. Different Components of SDTs 1.4. Scope of This Work and Contributions 2. Related Work and 2.1. Digital Twins and Variants 2.2. Spatial Digital Twin Case Studies 3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing 3.2. Data Modeling, Storage and Management 3.3. Big Data Analytics System 3.4. Maps and GIS Based Middleware 3.5. Key Functional Components 4. Other Relevant Modern Technologies and 4.1. AI & ML 4.2. Blockchain 4.3. Cloud Computing 5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition 5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT 5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis 5.6. Building Simulation Environment 5.7. Visualizing Complex and Diverse Interactions 5.8. Mitigating the Security and Privacy Concerns 6. Conclusion and References 2.2. Spatial Digital Twin Case Studies In this section, we present some live city digital twin systems from different part of the world, which will help us to understand the scope and relevant technologies of SDTs. 2.2.1. Singapore Singapore has created a 3D digital platform, called Virtual Singapore[5], that provides a semantic 3D model of the city. The model includes detailed information such as texture, material representation of geometrical objects, terrain attributes, and models of buildings that encode their geometry and components down to their fine details. The platform integrates data from various public agencies and co-ordinates it with existing geospatial and non-geospatial platforms to enrich the 3D model with 2D data and other information. Advanced information and modelling technology allows this spatial digital twin of Singapore to be infused with different sources of static, dynamic, and real-time city data and information, such as demographics, movement, and climate. Singapore digital twin offers four major capabilities: virtual experimentation; virtual test-bedding; planning and decision-making; and research and development. The twin can be used for virtual test-bedding or experimentation, such as examining the coverage areas of 3G/4G networks and providing realistic visualisation of poor coverage areas. It can also be used as a test-bedding platform to validate the provision of services, such as modelling and simulating crowd dispersion in the 3D model of the new sport hub to establish evacuation procedures during an emergency. With its rich data environment, Singapore digital twin is a holistic and integrated platform to develop analytical applications for planning and decision-making, such as analysing transport flows and pedestrian movement patterns. The platform’s rich data environment also allows researchers to innovate and develop new technologies or capabilities, such as advanced 3D tools. The digital twin has several potential use cases in tackling liveability issues. For example, the platform can be used for collaboration and decision-making by integrating various data sources, including data from government agencies, 3D models, information from the Internet, and real-time dynamic data from IoT devices. The digital twin allows different agencies to share and review the plans and designs of various projects in the same vicinity. Another potential use case is enhancing emergency response capabilities by modelling and simulating crowd dispersion and establishing evacuation procedures during an emergency. The digital twin can also be used to improve transportation planning by analysing transport flows and pedestrian movement patterns. 2.2.2. Zurich, Switzerland The Digital Twin project for the city of Zurich [31] is a cutting-edge initiative aimed at creating a virtual representation of the city’s physical environment. The project uses advanced technologies such as sensors, drones, and 3D modeling software to collect and integrate data on buildings, infrastructure, and public spaces. This data is then transformed into a 3D spatial model that creates a digital twin of the city. The digital twin serves as a virtual replica of the city, allowing planners, architects, and engineers to test and visualize different urban design scenarios, predict the impact of proposed changes, and improve the efficiency of urban management. The key features of the digital twin include the ability to simulate urban climate scenarios and analyze the interactions between the built environment and various urban systems. The project enables detailed visualization of urban planning scenarios, as well as concrete civil engineering and construction projects, including detailed building projects using BIM models. The platform also enhances collaboration between internal and external stakeholders, while leveraging technologies like virtual and augmented reality to provide more vivid visualizations of proposed projects. Ultimately, the digital twin project for Zurich is expected to enhance the city’s sustainability, resilience, and livability while fostering innovation and collaboration 2.2.3. NSW, Australia The New South Wales (NSW) spatial digital twin[6] is a 3D virtual model of the New South Wales state in Australia. It is an innovative initiative of the NSW Government that uses cutting-edge technology to create a digital representation of the state’s physical and geographical features. The digital twin is based on a range of data sources, including satellite imagery, LiDAR data, and other geographical data. These data sources are combined to create a high-fidelity, 3D model of the state’s infrastructure, built environment, natural environment, and other assets. The model is constantly updated in real-time as new data becomes available The NSW spatial digital twin is designed to support a range of applications and use cases. For example, it can be used to visualize and analyze urban planning scenarios. The 3D model can be used to visualize different urban planning scenarios, such as the impact of new buildings, roads, and other infrastructure projects. This can help planners and decision-makers to make informed decisions about development projects and their impact on the environment and community. It can also be used to monitor and manage infrastructure assets such as roads, bridges, and utilities. It can provide real-time information on the condition of these assets and help identify maintenance needs and potential risks. This digital twin can also be used to support emergency management and response efforts. For example, it can be used to simulate and analyze the impact of natural disasters, such as bushfires and floods, and help emergency responders to plan and coordinate their response efforts. The NSW Government is also exploring other potential use cases for the digital twin, such as supporting environmental monitoring and management, tourism, and cultural heritage preservation. Overall, the NSW Spatial Digital Twin is a powerful tool that has the potential to transform the way the state’s resources and assets are managed and utilized. 2.2.4. Boston, USA Boston has created a comprehensive digital twin of the city that incorporates a wide range of data, including building layouts, public transit routes, tree canopies, and proposed and under-construction buildings. The city began working on the twin in 2005, using the 3D modeling capabilities of geographic information system (GIS) software and the expertise of Esri professional services. The twin allows for testing of planning decisions before implementation in the real world, helping planners visualize the world in its existing form. It also includes tools for analyzing shadows and evaluating the impact of new zoning and development. This helps the Boston Planning & Development Agency (BPDA) ensure that projects adhere to the city’s zoning code height, density, and usage requirements. The qualitative analysis of the twin provides a quick visual to see shadows cast by any new building, while quantitative assessments provide more in-depth measurements, such as the extent and duration of shadows through the seasons. The city’s open data, including parcel ownership, zoning districts, historic landmarks, and open space, is integrated into the 3D model to analyze and evaluate city planning and development. BPDA uses planning and shadow tools to create real-world visualizations for a wide variety of decision-making tasks, including planning and development, flood modeling, shadow studies, and line-of-sight evaluation. Authors: (1) Mohammed Eunus Ali, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (2) Muhammad Aamir Cheema, Faculty of Information Technology, Monash University, 20 Exhibition Walk, Clayton, 3164, VIC, Australia; (3) Tanzima Hashem, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (4) Anwaar Ulhaq, School of Computing, Charles Sturt University, Port Macquarie, 2444, NSW, Australia; (5) Muhammad Ali Babar, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. [5] https://www.nrf.gov.sg/programmes/virtual-singapore [6] https://nsw.digitaltwin.terria.io/ Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1. Spatial Digital Twins (SDTs) 1.1. Spatial Digital Twins (SDTs) 1.2. Applications 1.2. Applications 1.3. Different Components of SDTs 1.3. Different Components of SDTs 1.4. Scope of This Work and Contributions 1.4. Scope of This Work and Contributions 2. Related Work and 2.1. Digital Twins and Variants 2. Related Work and 2.1. Digital Twins and Variants 2.2. Spatial Digital Twin Case Studies 2.2. Spatial Digital Twin Case Studies 3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing 3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing 3.2. Data Modeling, Storage and Management 3.2. Data Modeling, Storage and Management 3.3. Big Data Analytics System 3.3. Big Data Analytics System 3.4. Maps and GIS Based Middleware 3.4. Maps and GIS Based Middleware 3.5. Key Functional Components 3.5. Key Functional Components 4. Other Relevant Modern Technologies and 4.1. AI & ML 4. Other Relevant Modern Technologies and 4.1. AI & ML 4.2. Blockchain 4.2. Blockchain 4.3. Cloud Computing 4.3. Cloud Computing 5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition 5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition 5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT 5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT 5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis 5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis 5.6. Building Simulation Environment 5.6. Building Simulation Environment 5.7. Visualizing Complex and Diverse Interactions 5.7. Visualizing Complex and Diverse Interactions 5.8. Mitigating the Security and Privacy Concerns 5.8. Mitigating the Security and Privacy Concerns 6. Conclusion and References 6. Conclusion and References 2.2. Spatial Digital Twin Case Studies In this section, we present some live city digital twin systems from different part of the world, which will help us to understand the scope and relevant technologies of SDTs. 2.2.1. Singapore 2.2.1. Singapore 2.2.1. Singapore Singapore has created a 3D digital platform, called Virtual Singapore[5], that provides a semantic 3D model of the city. The model includes detailed information such as texture, material representation of geometrical objects, terrain attributes, and models of buildings that encode their geometry and components down to their fine details. The platform integrates data from various public agencies and co-ordinates it with existing geospatial and non-geospatial platforms to enrich the 3D model with 2D data and other information. Advanced information and modelling technology allows this spatial digital twin of Singapore to be infused with different sources of static, dynamic, and real-time city data and information, such as demographics, movement, and climate. Singapore digital twin offers four major capabilities: virtual experimentation; virtual test-bedding; planning and decision-making; and research and development. The twin can be used for virtual test-bedding or experimentation, such as examining the coverage areas of 3G/4G networks and providing realistic visualisation of poor coverage areas. It can also be used as a test-bedding platform to validate the provision of services, such as modelling and simulating crowd dispersion in the 3D model of the new sport hub to establish evacuation procedures during an emergency. With its rich data environment, Singapore digital twin is a holistic and integrated platform to develop analytical applications for planning and decision-making, such as analysing transport flows and pedestrian movement patterns. The platform’s rich data environment also allows researchers to innovate and develop new technologies or capabilities, such as advanced 3D tools. The digital twin has several potential use cases in tackling liveability issues. For example, the platform can be used for collaboration and decision-making by integrating various data sources, including data from government agencies, 3D models, information from the Internet, and real-time dynamic data from IoT devices. The digital twin allows different agencies to share and review the plans and designs of various projects in the same vicinity. Another potential use case is enhancing emergency response capabilities by modelling and simulating crowd dispersion and establishing evacuation procedures during an emergency. The digital twin can also be used to improve transportation planning by analysing transport flows and pedestrian movement patterns. 2.2.2. Zurich, Switzerland 2.2.2. Zurich, Switzerland 2.2.2. Zurich, Switzerland The Digital Twin project for the city of Zurich [31] is a cutting-edge initiative aimed at creating a virtual representation of the city’s physical environment. The project uses advanced technologies such as sensors, drones, and 3D modeling software to collect and integrate data on buildings, infrastructure, and public spaces. This data is then transformed into a 3D spatial model that creates a digital twin of the city. The digital twin serves as a virtual replica of the city, allowing planners, architects, and engineers to test and visualize different urban design scenarios, predict the impact of proposed changes, and improve the efficiency of urban management. The key features of the digital twin include the ability to simulate urban climate scenarios and analyze the interactions between the built environment and various urban systems. The project enables detailed visualization of urban planning scenarios, as well as concrete civil engineering and construction projects, including detailed building projects using BIM models. The platform also enhances collaboration between internal and external stakeholders, while leveraging technologies like virtual and augmented reality to provide more vivid visualizations of proposed projects. Ultimately, the digital twin project for Zurich is expected to enhance the city’s sustainability, resilience, and livability while fostering innovation and collaboration 2.2.3. NSW, Australia 2.2.3. NSW, Australia 2.2.3. NSW, Australia The New South Wales (NSW) spatial digital twin[6] is a 3D virtual model of the New South Wales state in Australia. It is an innovative initiative of the NSW Government that uses cutting-edge technology to create a digital representation of the state’s physical and geographical features. The digital twin is based on a range of data sources, including satellite imagery, LiDAR data, and other geographical data. These data sources are combined to create a high-fidelity, 3D model of the state’s infrastructure, built environment, natural environment, and other assets. The model is constantly updated in real-time as new data becomes available The NSW spatial digital twin is designed to support a range of applications and use cases. For example, it can be used to visualize and analyze urban planning scenarios. The 3D model can be used to visualize different urban planning scenarios, such as the impact of new buildings, roads, and other infrastructure projects. This can help planners and decision-makers to make informed decisions about development projects and their impact on the environment and community. It can also be used to monitor and manage infrastructure assets such as roads, bridges, and utilities. It can provide real-time information on the condition of these assets and help identify maintenance needs and potential risks. This digital twin can also be used to support emergency management and response efforts. For example, it can be used to simulate and analyze the impact of natural disasters, such as bushfires and floods, and help emergency responders to plan and coordinate their response efforts. The NSW Government is also exploring other potential use cases for the digital twin, such as supporting environmental monitoring and management, tourism, and cultural heritage preservation. Overall, the NSW Spatial Digital Twin is a powerful tool that has the potential to transform the way the state’s resources and assets are managed and utilized. 2.2.4. Boston, USA 2.2.4. Boston, USA 2.2.4. Boston, USA Boston has created a comprehensive digital twin of the city that incorporates a wide range of data, including building layouts, public transit routes, tree canopies, and proposed and under-construction buildings. The city began working on the twin in 2005, using the 3D modeling capabilities of geographic information system (GIS) software and the expertise of Esri professional services. The twin allows for testing of planning decisions before implementation in the real world, helping planners visualize the world in its existing form. It also includes tools for analyzing shadows and evaluating the impact of new zoning and development. This helps the Boston Planning & Development Agency (BPDA) ensure that projects adhere to the city’s zoning code height, density, and usage requirements. The qualitative analysis of the twin provides a quick visual to see shadows cast by any new building, while quantitative assessments provide more in-depth measurements, such as the extent and duration of shadows through the seasons. The city’s open data, including parcel ownership, zoning districts, historic landmarks, and open space, is integrated into the 3D model to analyze and evaluate city planning and development. BPDA uses planning and shadow tools to create real-world visualizations for a wide variety of decision-making tasks, including planning and development, flood modeling, shadow studies, and line-of-sight evaluation. Authors: (1) Mohammed Eunus Ali, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (2) Muhammad Aamir Cheema, Faculty of Information Technology, Monash University, 20 Exhibition Walk, Clayton, 3164, VIC, Australia; (3) Tanzima Hashem, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (4) Anwaar Ulhaq, School of Computing, Charles Sturt University, Port Macquarie, 2444, NSW, Australia; (5) Muhammad Ali Babar, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia. Authors: Authors: (1) Mohammed Eunus Ali, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (2) Muhammad Aamir Cheema, Faculty of Information Technology, Monash University, 20 Exhibition Walk, Clayton, 3164, VIC, Australia; (3) Tanzima Hashem, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (4) Anwaar Ulhaq, School of Computing, Charles Sturt University, Port Macquarie, 2444, NSW, Australia; (5) Muhammad Ali Babar, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [5] https://www.nrf.gov.sg/programmes/virtual-singapore https://www.nrf.gov.sg/programmes/virtual-singapore [6] https://nsw.digitaltwin.terria.io/ https://nsw.digitaltwin.terria.io/
