ABSTRACT. This paper provides a systematic literature review of studies investigating deep learning-based sensing and extended reality technologies, virtual simulation and data acquisition tools, and spatial cognition and neural network algorithms configuring virtual urban environments. Throughout April 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin cities and immersive 3D environments” + “deep learning-based sensing and extended reality technologies,” “visual recognition and geospatial mapping tools,” and “virtual simulation and spatial cognition algorithms.” As research published in 2022 and 2023 was inspected, only 144 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, Distiller SR, MMAT, and ROBIS.

Keywords: deep learning; extended reality; visual recognition; geospatial mapping; virtual simulation; spatial cognition; digital twin cities; immersive 3D environments

How to cite: Bugaj, M., Kliestik, T., Tudosă, P., and Lăzăroiu, G. (2023). “Deep Learning-based Sensing and Extended Reality Technologies, Visual Recognition and Geospatial Mapping Tools, and Virtual Simulation and Spatial Cognition Algorithms in Digital Twin Cities and Immersive 3D Environments,” Geopolitics, History, and International Relations 15(1): 31–45. doi: 10.22381/GHIR15120232.

Received 10 May 2023 • Received in revised form 24 June 2023
Accepted 26 June 2023 • Available online 30 June 2023