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
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

Summary/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.

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