Deep Learning-based Sensing and 3D Imaging Technologies, Machine Vision and Geospatial Mapping Tools, and Big Data-driven Urban Analytics in Cognitive Smart Cities
Raluca-Ștefania Balica*ABSTRACT. In this article, previous research findings were cumulated, indicating that computer vision systems, digital twin modeling and simulation tools, infrastructure virtualization and deep learning-based sensing technologies, and big geospatial data analytics. The contribution to the literature on sensing and computing technologies, data acquisition and virtual navigation tools, computer vision and virtual simulation algorithms, and urban big data analytics is by showing that immersive virtual experiences can be achieved by use of cognitive and behavioral technologies, multi-sensor fusion and perception systems, and digital twin simulation and deep generative modeling tools across interconnected digital realms. Throughout June 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “cognitive smart cities” + “deep learning-based sensing and 3D imaging technologies,” “machine vision and geospatial mapping tools,” and “big data-driven urban analytics.” As research published between 2022 and 2023 was inspected, only 159 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, CASP, MMAT, Rayyan, SWIFT-Active Screener, and Systematic Review Accelerator.
Keywords: deep learning; sensing; 3D imaging; machine vision; geospatial mapping; big data; urban analytics; cognitive smart city
How to cite: Balica, R.-Ș. (2023). “Deep Learning-based Sensing and 3D Imaging Technologies, Machine Vision and Geospatial Mapping Tools, and Big Data-driven Urban Analytics in Cognitive Smart Cities,” Geopolitics, History, and International Relations 15(2): 69–83. doi: 10.22381/GHIR15220235.
Received 16 July 2023 • Received in revised form 25 October 2023
Accepted 27 October 2023 • Available online 30 October 2023