ABSTRACT. This paper draws on a substantial body of theoretical and empirical research on monitoring and sensing technologies, deep learning and image recognition algorithms, and digital twin simulation and data visualization tools enabling extended reality environments. In this research, prior findings were cumulated indicating that immersive decentralized 3D digital worlds integrate sensory data mining techniques, deep learning artificial intelligence and image recognition tools, and immersive visualization systems. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout April 2022, with search terms including “Web3-powered metaverse worlds” + “spatial computing and deep learning-based sensing technologies,” “machine vision and cognitive decision-making algorithms,” and “digital twin simulation and data visualization tools.” As research published in 2022 was inspected, only 154 articles satisfied the eligibility criteria, and 30 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, ROBIS, and SRDR.

Keywords: spatial computing; deep learning; sensor; machine vision; cognitive decision-making; digital twin; simulation; data visualization; Web3; metaverse

How to cite: Zvarikova, K., Novak, A., and Atkinson, D. (2022). “Spatial Computing and Deep Learning-based Sensing Technologies, Machine Vision and Cognitive Decision-Making Algorithms, and Digital Twin Simulation and Data Visualization Tools in Web3-powered Metaverse Worlds,” Smart Governance 1(3): 7–22. doi: 10.22381/sg1320221.

Received 26 May 2022 • Received in revised form 25 September 2022
Accepted 29 September 2022 • Available online 30 September 2022