ABSTRACT. In this article, previous research findings were cumulated, indicating that 3D modeling and simulation technologies, virtual navigation and cloud-based object tracking tools, and visual sensing and perception systems configure synthetic digitally-mediated environments in the industrial metaverse. Throughout April 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “hyper-immersive simulated environments” + “digital twin extended reality and industrial generative artificial intelligence technologies,” “virtual 3D scene simulation and interactive 3D geo-visualization systems,” and “spatio‒temporal fusion and deep reinforcement learning algorithms.” As research published in 2022 and 2023 was inspected, only 170 articles satisfied the eligibility criteria, and 19 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: ASReview Lab, CADIMA, Eppi-Reviewer, Litstream, MMAT, and Rayyan.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: digital twin extended reality; industrial generative artificial intelligence; virtual 3D scene simulation; interactive 3D geo-visualization; spatio‒temporal fusion; deep reinforcement learning

How to cite: Popescu, G. H., Abdullah, F., Stamule, S., Bogdan, A., and Staicu, D. (2024). “Digital Twin Extended Reality and Industrial Generative Artificial Intelligence Technologies, Virtual 3D Scene Simulation and Interactive 3D Geo-Visualization Systems, and Spatio‒Temporal Fusion and Deep Reinforcement Learning Algorithms in Hyper-Immersive Simulated Environments,” Journal of Self-Governance and Management Economics 12(3): 24–38. doi: 10.22381/jsme12320242.

Received 19 May 2024 • Received in revised form 21 September 2024
Accepted 26 September 2024 • Available online 29 September 2024