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. 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.

翻译：数字孪生(DT)是物理对象或系统的虚拟副本，旨在监测、分析并优化其行为与特性。空间数字孪生(SDT)作为数字孪生的一种特殊类型，强调物理实体的地理空间维度，通过精确的位置与尺度属性，在空间环境中实现对其的全面理解。当前关于SDT的研究主要集中于分析其在各应用领域的潜在影响与机遇。由于构建SDT涉及复杂流程并需整合多种空间计算技术，这一多学科领域的研究者与实践者往往难以深入理解其底层赋能技术的核心细节。本文首次采用分层方法（从数据采集到可视化）系统性地分析了与构建SDT相关的各类空间技术。具体而言，我们将SDT的关键组件划分为四个技术层次：(i) 数据采集；(ii) 空间数据库管理与大数据分析系统；(iii) GIS中间件软件、地图与应用程序接口(API)；(iv) 核心功能组件（如可视化、查询、挖掘、模拟与预测）。此外，我们探讨了人工智能/机器学习、区块链与云计算等现代技术如何有效赋能并增强SDT。最后，本文识别了SDT领域的多项研究挑战与机遇。本研究为SDT研究者与实践者提供了重要参考——它清晰区分了SDT与传统DT，识别了其独特应用场景，勾勒了核心技术组件，并展望了未来发展方向及面临的挑战。