ABSTRACT. In this article, previous research findings were cumulated indicating that computer vision artificial intelligence and robotic navigation systems, deep learning-based multi-source data fusion, and simulated augmented reality and machine vision technologies configure edge artificial intelligence-based metaverse architectures in interconnected digital realms. Throughout June 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “interoperable extended reality environments” + “urban sensing and smart city technologies,” “visual localization and mapping devices,” and “real-time object detection and geospatial modeling systems.” As research published in 2022 and 2023 was inspected, only 166 articles satisfied the eligibility criteria, and 20 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: BIBOT, Citationchaser, Colandr, METAGEAR package for R, ROBIS, and SWIFT-Active Screener.

Keywords: urban sensor; smart city; visual localization; mapping device; real-time object detection; geospatial modeling

How to cite: Lăzăroiu, G., Pera, A., and Balica, R.-Ș. (2024). “Urban Sensing and Smart City Technologies, Visual Localization and Mapping Devices, and Real-Time Object Detection and Geospatial Modeling Systems in Interoperable Extended Reality Environments,” Geopolitics, History, and International Relations 16(1): 83–97. doi: 10.22381/GHIR16120245.

Received 11 July 2024 • Received in revised form 20 October 2024
Accepted 26 October 2024 • Available online 30 October 2024