ABSTRACT. This paper draws on a substantial body of theoretical and empirical research on geospatial mapping and simulation modeling tools, digital twin and machine vision technologies, and big data computing and remote sensing systems for smart city logistics in immersive and interactive virtual environments. Based on an in-depth survey of the literature, the purpose of the paper is to explore how urban digital governance and big geospatial data analytics in Internet of Things-enabled smart cities necessitate digital twin simulation and modeling tools, big data-driven artificial intelligence and deep learning-based sensing technologies, and cognitive computing and wireless network systems. The review software systems harnessed for article screening and quality evaluation include AMSTAR, CADIMA, R package and Shiny app citationchaser, DistillerSR, Eppi-Reviewer, JBI SUMARI, Litstream, MMAT, Nested Knowledge, PICO Portal, Rayyan, and SluRp. The case study covers Singapore’s artificial intelligence-based data-driven decision making and Internet of Things sensor deployment.

Keywords: geospatial mapping; digital twin; machine vision; big data computing; remote sensing; smart city logistics

How to cite: Nica, E., Khamis, A., Crețu, S., Dobrotă, E. M., and Grofcikova, J. (2025). “Geospatial Mapping and Simulation Modeling Tools, Digital Twin and Machine Vision Technologies, and Big Data Computing and Remote Sensing Systems for Smart City Logistics in Immersive and Interactive Virtual Environments,” Geopolitics, History, and International Relations 17(2): 29–40. doi: 10.22381/GHIR17220252.

Received 19 July 2025 • Received in revised form 23 October 2025
Accepted 25 October 2025 • Available online 30 October 2025