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.
Table of Links
1.1. Spatial Digital Twins (SDTs)
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
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
Abstract
A Digital Twin (DT) is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A Spatial Digital Twin (SDT) is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity, incorporating precise location and dimensional attributes for a comprehensive understanding within its spatial environment. With the recent advancement in spatial technologies and breakthroughs in other computing technologies such as AI/ML, the SDTs market is expected to rise to $25 billions covering a wide range of applications. The majority of existing research focuses on DTs and often fails to address the necessary spatial technologies essential for constructing SDTs. The current body of research on SDTs primarily concentrates on analyzing their potential impact and opportunities within various application domains. As building an SDT is a complex process and requires a variety of spatial computing technologies, it is not straightforward for practitioners and researchers of this multi-disciplinary domain to grasp the underlying details of enabling technologies of the SDT. In this paper, we are the first to systematically analyze different spatial technologies relevant to building an SDT in layered approach (starting from data acquisition to visualization). More specifically, we present the key components of SDTs into four layers of technologies: (i) data acquisition; (ii) spatial database management & big data analytics systems; (iii) GIS middleware software, maps & APIs; and (iv) key functional components such as visualizing, querying, mining, simulation and prediction. Moreover, we discuss how modern technologies such as AI/ML, blockchains, and cloud computing can be effectively utilized in enabling and enhancing SDTs. Finally, we identify a number of research challenges and opportunities in SDTs. This work serves as an important resource for SDT researchers and practitioners as it explicitly distinguishes SDTs from traditional DTs, identifies unique applications, outlines the essential technological components of SDTs, and presents a vision for their future development along with the challenges that lie ahead.
1. Introduction
A digital twin is a virtual (or mirror) representation of a physical real-world entity, or system. According to Glaessegen et al. [1], a digital twin is an integrated software system that mirrors the life of its corresponding physical object. With the rapid digital technological growth in the last decade, the digital twins have received significant attention in many application domains including manufacturing, agriculture, healthcare, and smart & sustainable cities. Major aims of these digital twins solutions are to improve the performance and efficiency of the system by real-time monitoring and predictive maintenance of physical entities, and by generating useful insights and giving feedback to the real-world entities for optimizing operational efficiencies. As the fastest growing sector of the fourth industrial revolution, the digital twins market is expected to grow from USD 12.7 billions in 2021 to USD 45 billions by 2026 [2]. Thus, we have witnessed a plethora of research and development involving digital twins in both academia and industries [3, 4].
The concept of digital twin was first introduced by NASA scientists to mirror the life cycle of space vehicle in 2012 [1] and then later successfully applied in the field of manufacturing to improve the performance in manufacturing pipeline [3, 5]. After that, the digital twin technology evolved in many directions – one such area is smart and sustainable cities/precincts. Developing smart and sustainable cities is crucial as more than half of the world population lives in cities, responsible for 70% of the green house gas emissions1. Thus, modeling urban environment and development, optimizing operational efficiencies, and enhancing decision-making are the keys to develop sustainable cities. To achieve the above goals, digital twins have evolved around geo-spatial context to give birth to a new type of digital twin, called spatial digital twins in this paper.
This paper is
[1] https://www.worldbank.org/en/topic/urbandevelopment/brief/global-platform-for-sustainable-cities