A Data-Driven Solution for Large-Scale Open-Pit Mines Excavation Monitoring Based on 3D Point Cloud

We present an adaptive point cloud workflow that withstands heavy environmental noise and the large datasets typical of open-pit mines. The workflow automatically tunes its parameters from the statistics of each input scene, eliminating manual parameter tuning. For instance, it sets the ICP correspondence distance and the clustering threshold without user input. Additionally, our method integrates a coarse-to-fine registration strategy, robust change detection, and precise volumetric estimation based on digital elevation models. Experiments on simulated mining datasets show our method remains robust under heavy noise and misalignment, with volume errors consistently below $2\%$. A field pilot study at a limestone quarry further underscores its practical reliability and operational robustness. This research provides a precise, automated solution for real-time mining monitoring, effectively advancing sustainable and intelligent mining practices. Source code and datasets are publicly available at github.com/deemoe404/volcal_baseline.