Urban Sensing and Digital Twin Technologies, Deep Learning and Simulation Modeling Algorithms, and Geospatial Data Mining and Virtual Mapping Tools for Smart City Governance
Urban Sensing and Digital Twin Technologies, Deep Learning and Simulation Modeling Algorithms, and Geospatial Data Mining and Virtual Mapping Tools for Smart City Governance

Summary/Abstract: The purpose of this study is to examine urban digital twins, extended reality and machine vision technologies, immersive visualization systems, and realtime predictive analytics. In this article, previous research findings were cumulated indicating that interactive virtual environments integrate environment perception sensors, big geospatial data and sentiment analytics, and immersive 3D and deep learning-based sensing technologies. The contribution to the literature on algorithm-driven sensing devices, cognitive data fusion techniques, computer vision and simulation modeling algorithms, and digital twin simulation modeling and deep learning-based ambient sound processing tools is by showing that extended reality environments require interoperable virtual and wireless sensor networks, spatial computing and image acquisition devices, and geospatial intelligence and data mining tools. Throughout July 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “smart city governance” + “urban sensing and digital twin technologies,” “deep learning and simulation modeling algorithms,” and “geospatial data mining and virtual mapping tools.” As research published between 2022 and 2023 was inspected, only 162 articles satisfied the eligibility criteria, and 21 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, CADIMA, Citationchaser, JBI SUMARI, ROBIS, and SRDR+.

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