ABSTRACT. This paper draws on a substantial body of theoretical and empirical research on predictive analytics modeling and virtual mapping tools, urban sensing and edge computing technologies, big geospatial data analytics, and visual perception and virtual machine algorithms. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout January 2025, with search terms including “the metaverse decentralized and Internet of Things sensing infrastructure of immersive 3D environments” + “big data-driven urban geopolitics,” “realistic 3D scene construction and cognitive automation technologies,” and “predictive maintenance and context-aware deep reinforcement learning algorithms.” As research published in 2022 and 2023 was inspected, only 166 articles satisfied the eligibility criteria, and 18 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: CASP, Nested Knowledge, ROBIS, SluRp, SWIFT-Active Screener, and Systematic Review Accelerator.

Keywords: urban geopolitics; realistic 3D scene construction; context-aware deep reinforcement learning; metaverse; Internet of Things; immersive 3D environments

How to cite: Popescu, G. H., Poliakova, A., Ciurlău, C. F., Erokhin, V., and Stana, C. (2025). “Big Data-driven Urban Geopolitics, Realistic 3D Scene Construction and Cognitive Automation Technologies, and Predictive Maintenance and Context-Aware Deep Reinforcement Learning Algorithms in the Metaverse Decentralized and Internet of Things Sensing Infrastructure of Immersive 3D Environments,” Geopolitics, History, and International Relations 17(1): 47–61. doi: 10.22381/GHIR17120253.

Received 24 February 2025 • Received in revised form 22 June 2025
Accepted 25 June 2025 • Available online 30 June 2025