UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data

TL;DR

This paper introduces an integrated UAV-based system combining LiDAR and deep learning for accurate, geo-referenced object detection and positioning in open-pit mining environments, enhancing digital twin integration and operational safety.

Contribution

It presents a novel, scalable system architecture that integrates UAV sensing, 3D modeling, and AI for precise spatial data collection in mining, advancing beyond traditional methods.

Findings

Demonstrated high-accuracy object detection in mining environments

Achieved scalable, automated spatial data collection

Supported integration into digital twin platforms

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 modeling, 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 localization, 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. While 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.