Ties
Summary and 1 Introduction
1.1. Digital Spatial Twins (SDTS)
1.2. Applications
1.3. Different components of SDTs
1.4. Scope of this work and contributions
2. Related works and 2.1. Twins and digital variants
2.2. Case studies with digital spatial twins
3. Blockons for construction of digital spatial twins and 3.1. Data acquisition and processing
3.2. Modeling, storage and data management
3.3. Big Data analysis system
3.4. GIS -based maps and 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. Acquisition of multimodal and multi-resolution data
5.2. NLP for space requests and 5.3. Comparative analysis of databases and the Big Data platform for SDT
5.4. Automated spatial information and 5.5. Multimodal analysis
5.6. Construction simulation environment
5.7. View complex and various interactions
5.8. Alleviate security and confidentiality problems
6. Conclusion and references
The concept of digital twin appeared in the early 2000s [1] and has since evolved to include various forms such as the digital spatial twin [2]Digital Twin Mobility [16, 17]Urban Digital Twin, Human Digital Twin and others. In section 2.1, we discuss different variants of digital twins and in section 2.2, we discuss several case studies for SDTs.
2.1. Twins and digital variants
In [18]The generalized characteristics of a digital twin are identified in terms of three main components: physical reality, virtual representation and interconnections that exchange information between physical reality and virtual representation. A recent report [2] focuses on the importance of integrating spatial data with the digital twin and describes a spatial digital twin as a dimensional holistic representation and based on the location of assets, infrastructures and systems. Mobility Digital Twin and Smart City Digital Twin are examples of spatial digital twin. Other types of digital twins include the digital twin of industry, the HEalth digital twin and others.
Digital mobility twins. In [16]The authors have developed a holistic framework for digital mobility twins which manage mobility entities such as humans, vehicle and traffic. In [17]The authors proposed a system of prediction of human mobility with fine grain for digital mobility twins. Digital mobility twins are an example of digital spatial twins, as one of their main objectives is to view and predict the motion locations of mobility entities in virtual space.
Digital twins of the smart city. Digital Twin has been widely explored in the context of intelligent cities. Smart City Digital Twin is also known as Twin Urban Digital. The main challenges for the implementation of the digital twin for intelligent cities come from the complexity of systems and types of final variant users [19]. [9] shows that urban planners, political stakeholders and other decision-makers can be very beneficiaries of digital Twin for intelligent cities, which is simply trained by integrating real-time data with an existing multi-aging framework. [20] Presents the study of detailed literature on the integration of BIM and GIS data in the context of the city's digital city. They also provide a theoretical analysis and design the framework and highlight future research orientations in the field of GIS and BIM integration. [21] highlights the potential of automatic learning techniques for the treatment of 3D points clouds for the construction of geospatial digital twins. [22] Presents an examination of the existing work on the digital twins of the city and also identifies the potential and the current and potential challenges of the digital twin cities.
Digital industry twins. Digital twins were taken into account for the automation of manufacturing in industries before being applied to smart cities. Researchers focused on the adoption of digital twins technologies from different dimensions for the design and optimization of the family of product products [23] and production of production [24]. A recent study [25] Identifies and assesses the importance of key functionalities (ie digital modeling functionality, analysis support function, aptitude update function and control function) of the digital twin for the success of its implementation manufacturing.
HEALTH digital twin. A human digital twin [26]Also known as Twin Digital Health, practically models the life cycle of a human in order to manage intelligent health. In [27]The authors proposed to use the augmented reality augmented digital twin helped to model the human digital twin. Digital health twins have improved personalized health care [28] Due to the availability of large -scale data for the risk prediction process and the progression of the disease.
Others. The concept of digital twin has also been implemented for the interior space [29] To capture the behavior of the gaze of a class. In [30]The authors studied the integration of digital twins technologies into four levels of energy sector applications (i.e. the low carbon storage system, intelligent network, electrified transport and advanced energy storage), identified their challenges and discussed possible solutions.
Authors:
(1) Mohammed Eunus Ali, IT and Engineering Department, Bangladesh University of Engineering and Technology, Ece Building, Dhaka, 1000, Bangladesh;
(2) Muhammad Aamir Cheema, Faculty of Information Technology, Monash University, 20 Walk Walk, Clayton, 3164, Vic, Australia;
(3) Tanzima Hachem, 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 IT and Mathematics, University of Adelaide, Adelaide, 5005, SA, Australia.
