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 1.1. Spatial Digital Twins (SDTs) A spatial digital twin (SDT) essentially is a mirror representation of the real-world geospatial objects (e.g, buildings, roads) and systems (e.g., environmental or traffic monitoring). The geospatial consortium formally defines an SDT as a virtual representation of real-world entities and processes with precise location and dimensional attributes included in the model, where the virtual model is updated at a synchronized frequency [2]. At a high level, both SDTs and traditional digital twins (DTs) have many similarities, where both maintain virtual representation of real-world objects to assist monitoring, planning, and decision making with predictive capabilities. The key differentiating factor of SDTs from DTs is that SDTs incorporate spatial context to capture and provide location and relative dimensional representation of geospatial objects and processes. More specifically, we envisage several key objectives of an SDT, which are: (i) to visualize, monitor, assess, and forecast the state and activities of different objects in a spatial region (e.g., monitoring and predicting energy consumption of buildings); (ii) to predict the system response and to uncover previously unknown insights from the near real-time and historical data (e.g., prediction of flood in next 24 hours); (iii) to simulate what-if scenarios to uncover previously unknown insights and to recommend corrective measure; and iv) to give feedback to the physical entities or processes to take necessary corrective measures (e.g., using real-time traffic feed, a digital twin system can control the timing of traffic lights to resolve/avoid traffic jams). 1.2. Applications While SDTs are often mistakenly confused with 3D modeling and visualization techniques used for urban planning and development, the capabilities of SDTs extend far beyond these applications, encompassing a broad range of functionalities with numerous potential uses. We have listed some important application areas as follows. • Urban Planing and Management: The spatial digital twin of a city can be used for monitoring and managing urban planning and development processes. For example, Boston city digital twin[2] is used to evaluate how much shadow a proposed high-rise building will cast on a given park area (see Figure 1(a)). • Urban Planing and Management: • Natural Disaster: In recent years, the scale and frequency of natural disasters such as floods and bushfires have seen a significant increase worldwide, largely attributed to the effects of climate change. For example, in March 2020, bushfire in Australia killed 3 billion animals, burnt more than 18 million hectors of land, and destroyed over 3000 houses[3]. Using spatial digital twins of forests, animals, buildings, and other infrastructure, a better rescue and fire containment plan can be made, which can significantly reduce the extent of lives and property damages. An example of SDT-based fire rescue system[4] is shown in Figure 1(b). • Natural Disaster: • Virtual and Augmented Reality Based Service: SDTs hold tremendous potential in the realm of virtual reality, enabling users to simulate and interact with virtual models incorporating real-world entities. Additionally, they offer exciting possibilities in augmented reality, allowing users to seamlessly interact with and manipulate physical elements of the real world. For example, an electrician can see the layout of the wiring of the building retrieved from the SDT before repairing the fault. • Virtual and Augmented Reality Based Service: • Greenhouse Gas Emissions and Climate Change: Greenhouse Gas Emissions (GHG) is one of the major factors causing climate change and has a profound impact on our environment. To reduce GHG, which comes from fossil fuel consumption and other indirect source like electricity generation, it is important to monitor, manage, and plan for both macro and micro level energy consumption. SDTs can be helpful to monitor and manage current energy consumption, predict future consumption, and optimize the use of renewable energy sources in buildings, suburbs, or entire cities. By leveraging SDTs, we can work towards reducing GHG emissions, promoting sustainability, and fostering the transition to a more environmentally conscious energy landscape. • Greenhouse Gas Emissions and Climate Change: • Intelligent Transportation: SDTs can be used to operate on-demand vehicles on optimized routes, improve road safety, and reduce congestion by real-time monitoring and predictive analysis. • Epidemiology and Public Health: We have witnessed an unprecedented loss of lives in COVID19. Also, we have seen that this disease can be better managed by applying advanced data analytics and AI techniques on data collected from various sources [6]. An SDT can be an assistive tool for spatio-temporal prediction of the infectious disease, identifying the source of the diseases, tracking the movement of infected patients, and making a better plan for containing the disease. • Epidemiology and Public Health: • Smart or Precision Agriculture: Smart or precision agriculture that relies on sensing technologies to get real-time agricultural field data and AI&ML technique for decision making can be immensely benefited by the SDT developed around crop cultivation, distribution, and market demand. • Smart or Precision Agriculture: 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
