Virtual Modeling and Visual Computing Technologies, Internet of Things-based Decision Support and Remote Sensing Systems, and Context Awareness and Spatio-Temporal Fusion Algorithms in the Immersive Industrial Metaverse.

• Author: Goodman, Charles

1. Introduction

   Immersive 3D and augmented reality experiences can be attained by use of machine learning and visual perception algorithms, biometric and behavioral data, and automated speech recognition and language modeling tools. The purpose of our systematic review is to examine the recently published literature on the immersive industrial metaverse and integrate the insights it configures on virtual modeling and image-based visual computing technologies, Internet of Things-based decision support and remote sensing systems, and context awareness and spatio-temporal fusion algorithms. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that immersive metaverse experiences can be achieved by use of monitoring and sensing technologies (Grupac and Lazaroiu, 2022), 3D modeling and synthetic data tools, and real-time predictive and big geospatial data analytics. The actuality and novelty of this study are articulated by addressing immersive decentralized 3D digital worlds and the industrial metaverse, that is an emerging topic involving much interest. Our research problem is whether a fully connected metaverse develops on real-time predictive and sentiment analytics, deep learning-based ambient sound processing, and sensing and computing technologies.

   In this review, prior findings have been cumulated indicating that automated speech recognition and Internet of Things-based decision support systems, data mining and visualization tools (Zvarikova et al., 2023), and cloud-and semantic-based cognitive technologies configure immersive 3D virtual environments and the industrial metaverse. The identified gaps advance virtual navigation and data mining tools, virtual modeling and cognitive enhancement technologies, and visual perception and deep learning algorithms. Our main objective is to indicate that eye-tracking and image recognition technologies, computer vision and cognitive artificial intelligence algorithms (Kliestik et al., 2022), and virtual navigation and spatial data visualization tools further extended reality environments. This systematic review contributes to the literature on image recognition and spatial computing technologies (Valaskova et al., 2022), 3D virtual space networking and geospatial mapping tools (Machova et al., 2022), and cognitive artificial intelligence and deep learning algorithms by clarifying that Web3 technologies, smart manufacturing processes, and virtual object behavior are instrumental in augmented reality-based remotely-supported collaborative maintenance across simulated 3D modeling and augmented reality environments.

2. Theoretical Overview of the Main Concepts

   Intelligent connectivity infrastructures in the blockchain-based metaverse integrate augmented reality visual assets, context-aware augmented reality systems, and simulated augmented reality technology. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), geospatial mapping and digital twin technologies, 3D image modeling and synthetic data tools, and computer vision and cognitive artificial intelligence algorithms in the industrial metaverse (section 4), machine learning and visual perception algorithms, 3D virtual space networking and geospatial mapping tools, and biometric and behavioral data in the industrial metaverse (section 5), data mining and machine learning-based image recognition tools, monitoring and sensing technologies, and real-time predictive and big geospatial data analytics in the industrial metaverse (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   Throughout August 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "the immersive industrial metaverse" + "virtual modeling and image-based visual computing technologies," "Internet of Things-based decision support and remote sensing systems," and "context awareness and spatiotemporal fusion algorithms." As research published in 2022 was inspected, only 151 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 17 mainly empirical sources (Tables 1 and 2). Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR (Figures 1-6).

4. Geospatial Mapping and Digital Twin Technologies, 3D Image Modeling and Synthetic Data Tools, and Computer Vision and Cognitive Artificial Intelligence Algorithms in the Industrial Metaverse

   Virtual navigation and data mining tools, virtual modeling and cognitive enhancement technologies, and visual perception and deep learning algorithms (Cho et al., 2023; Mourtzis et al., 2023; Wang et al., 2023) enable immersive decentralized 3D digital worlds. Automated speech recognition and Internet of Things-based decision support systems, data mining and visualization tools, and cloud- and semantic-based cognitive technologies configure immersive 3D virtual environments and the industrial metaverse.

   Autonomous cognitive and remote sensing systems, automated speech recognition and data visualization tools, and geospatial mapping and digital twin technologies (Bhattacharya et al., 2023; Jaimini et al., 2022; Wang et al., 2023) configure extended reality...