UAV Object Detection and Positioning in a Mining Industrial Metaverse With Custom Geo‐Referenced Data
Vasiliki E. Balaska, Ioannis Tsampikos Papapetros, Katerina Maria Oikonomou, Loukas Bampis, Antonios C. Gasteratos · IET Cyber-Physical Systems Theory & Applications · 2026
ABSTRACT The mining sector increasingly adopts digital tools to improve operational efficiency, safety and data‐driven decision‐making. One of the key challenges remains the reliable acquisition of high‐resolution geo‐referenced spatial information to support core activities such as extraction planning and on‐site monitoring. This work presents an integrated system architecture that combines UAV‐based sensing, LiDAR terrain modelling and deep learning‐based object detection to generate spatially accurate information for open‐pit mining environments. The proposed pipeline includes geo‐referencing, 3D reconstruction and object localisation, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real‐world industrial contexts. Although the current implementation operates in post‐flight batch mode, it lays the foundation for real‐time extensions. The system contributes to the development of AI‐enhanced remote sensing in mining by demonstrating a scalable and field‐validated geospatial data workflow that supports situational awareness and infrastructure safety. The system operates within a cyber‐physical framework, linking UAV‐based sensing with AI‐driven analytics and digital‐twin feedback mechanisms to enhance perception and decision‐making in dynamic industrial environments.