Application of remote sensing and digital technologies in monitoring mining infrastructure

Abstract

Monitoring mining infrastructure is essential for ensuring operational safety and minimizing environmental risks associated with subsidence and structural instability. However, existing approaches often rely on single-source data, limiting their ability to capture complex and dynamic deformation processes. This study proposes an integrated multi-source monitoring framework that combines satellite-based Interferometric Synthetic Aperture Radar (InSAR), unmanned aerial vehicle (UAV) observations, ground-based measurements, and advanced digital analysis techniques. The methodology integrates multi-temporal InSAR time-series analysis with high-resolution UAV-derived data within a unified geospatial environment, supported by machine learning models for pattern recognition and prediction. The results reveal clear spatial and temporal deformation patterns in mining areas, with subsidence rates up to −50 mm/year in active extraction zones. Time-series analysis shows that deformation evolves nonlinearly, underscoring the importance of continuous monitoring for early risk detection. Validation against ground-based measurements confirms the reliability of the proposed approach, with root-mean-square error (RMSE) values in the range of 1–2 mm. The integration of multi-source data significantly improves monitoring accuracy and enables detailed analysis across different spatial scales. Furthermore, the application of machine learning techniques enhances predictive capability, allowing identification of potential instability zones before critical failure occurs.