Enabling spatial digital twins: Technologies, challenges, and future research directions

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 of its spatial environment. With the recent advancement in spatial technologies and breakthroughs in other computing technologies such as Artificial Intelligence (AI) and Machine Learning (ML), the SDTs market is expected to rise to 25 billion, 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 a layered approach (starting from data acquisition to visualization). More specifically, we present the tech stack of SDTs into five distinct layers of technologies: (i) data acquisition and processing; (ii) data integration, cataloging, and metadata management; (iii) data modeling, database management & big data analytics systems; (iv) Geographic Information System (GIS) software, maps, & APIs; and (v) 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.