DEVELOPING OPTIMIZED DRONE SYSTEMS FOR SURFACE-MINE SLOPE MONITORING AND EARLY-STAGE CRACK DETECTION

Surface-mine slope stability remains one of the most critical determinants of operational safety, production
continuity, and geotechnical risk management. Traditional monitoring techniques such as periodic total-station
measurements, ground-based LiDAR, and manual inspections often struggle to capture subtle deformation
patterns or early-stage cracking that evolve rapidly across large, irregular slope faces. As mines expand laterally
and vertically, these limitations create blind spots in hazard detection, increasing vulnerability to slope failures,
equipment losses, and worker endangerment. Emerging advances in drone-based sensing systems provide a
transformative pathway for enhancing the precision, speed, and spatial reach of slope-monitoring programs. From
a broader perspective, drone platforms equipped with high-resolution LiDAR, multispectral imaging, thermal
sensors, and visual–inertial SLAM technologies deliver dense spatial datasets that can detect micro-fractures,
bench-wall deformations, rock-mass discontinuities, and subtle thermal anomalies indicative of impending
instability. These unmanned systems significantly reduce data-collection time while improving access to steep
highwalls, remote benches, and geotechnically sensitive areas that pose challenges for ground crews. Narrowing
the focus, optimized drone systems tailored for slope monitoring rely on advanced algorithms for crack
segmentation, change detection, and temporal deformation tracking. Machine-learning models enhance earlystage crack identification by analyzing geometric irregularities, reflectance variations, and spectrothermal
gradients that precede visible failure mechanisms. When incorporated into digital-twin slope models and real-time
geotechnical dashboards, drone-derived data enables proactive decision-making, targeted reinforcement, and
predictive risk alerts. By integrating sensor optimization, efficient flight-path planning, scalable data processing,
and AI-driven analytics, next-generation drone systems redefine the future of surface-mine slope surveillance.
These capabilities transform slope monitoring from intermittent observation into a continuous, predictive, and
highly automated safety intelligence framework.