ABSTRACT. The purpose of this study is to examine spatial data analytics, urban modeling and simulation tools, 6G Internet of Things fog computing and digital twin technologies, and visual localization and mapping devices. Throughout June 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “sustainable smart cities” + “big data-driven digital twin and spatial computing technologies,” “artificial intelligence robotic and multi-sensor fusion systems,” and “deep learning-based image processing and augmented reality algorithms.” As research published in 2022 and 2023 was inspected, only 154 articles satisfied the eligibility criteria, and 20 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, CADIMA, METAGEAR package for R, MMAT, SluRp, and SWIFT-Active Screener.

Keywords: sustainable smart city; digital twin; spatial computing; artificial intelligence; multi-sensor fusion; image processing

How to cite: Nica, E., Hysa, E., Bîgu, A.-M., AlAkoum, A., and Melenciuc, M. (2024). “Big Data-driven Digital Twin and Spatial Computing Technologies, Artificial Intelligence Robotic and Multi-Sensor Fusion Systems, and Deep Learning-based Image Processing and Augmented Reality Algorithms in Sustainable Smart Cities,” Geopolitics, History, and International Relations 16(1): 98–112. doi: 10.22381/GHIR16120246.

Received 7 July 2024 • Received in revised form 19 October 2024
Accepted 26 October 2024 • Available online 30 October 2024