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A Digital Twin (DT) is a virtual replica of a physical object or system,created to monitor, analyze, and optimize its behavior and characteristics. ASpatial Digital Twin (SDT) is a specific type of digital twin that emphasizesthe geospatial aspects of the physical entity, incorporating precise locationand dimensional attributes for a comprehensive understanding within its spatialenvironment. The current body of research on SDTs primarily concentrates onanalyzing their potential impact and opportunities within various applicationdomains. As building an SDT is a complex process and requires a variety ofspatial computing technologies, it is not straightforward for practitioners andresearchers of this multi-disciplinary domain to grasp the underlying detailsof enabling technologies of the SDT. In this paper, we are the first tosystematically analyze different spatial technologies relevant to building anSDT in layered approach (starting from data acquisition to visualization). Morespecifically, we present the key components of SDTs into four layers oftechnologies: (i) data acquisition; (ii) spatial database management \& bigdata analytics systems; (iii) GIS middleware software, maps \& APIs; and (iv)key functional components such as visualizing, querying, mining, simulation andprediction. Moreover, we discuss how modern technologies such as AI/ML,blockchains, and cloud computing can be effectively utilized in enabling andenhancing SDTs. Finally, we identify a number of research challenges andopportunities in SDTs. This work serves as an important resource for SDTresearchers and practitioners as it explicitly distinguishes SDTs fromtraditional DTs, identifies unique applications, outlines the essentialtechnological components of SDTs, and presents a vision for their futuredevelopment along with the challenges that lie ahead.

Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions