ABSTRACT. The purpose of this study is to examine location-based predictive and visual cognitive algorithms, immersive metaverse and holographic telepresence technologies, and digital twin-enabled edge and intelligent sensing networks. I contribute to the literature on digital twin data modeling and visualization, location intelligence data, and ambient intelligence and digital simulation technologies by showing that ambient intelligence environments necessitate hyper-realistic immersive 3D simulations, sentiment recognition and remote sensing technologies, and data mining and visual attention modeling tools. Throughout May 2023, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “extended reality environments” + “machine learning-based predictive and virtual mapping algorithms,” “immersive metaverse and holographic telepresence technologies,” and “3D generative modeling and multiscale spatial data processing tools.” As I inspected research published in 2022 and 2023, only 177 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 34, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR.

Keywords: machine learning; predictive and virtual mapping algorithms; immersive metaverse; holographic telepresence technologies; 3D generative modeling; multiscale spatial data processing tools; extended reality environments

How to cite: Henley, S. (2023). “Machine Learning-based Predictive and Virtual Mapping Algorithms, Immersive Metaverse and Holographic Telepresence Technologies, and 3D Generative Modeling and Multiscale Spatial Data Processing Tools in Extended Reality Environments,” Review of Contemporary Philosophy 21: 154–171. doi: 10.22381/RCP2220239.

Received 21 June 2023 • Received in revised form 23 August 2023
Accepted 27 August 2023 • Available online 30 August 2023