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Aug 2026

Application of remote sensing and digital technologies in monitoring mining infrastructure

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.

Nurbek Abdixamidov, Maksuda Juraeva, Nafosat Meyliyeva et al. · 0 citations
Open access Aug 2026

Basin-Wide Monitoring and PIM Parameter Inversion of Mining Subsidence Using UAV-LiDAR

Accurate, comprehensive, and spatially continuous monitoring of mining-induced surface subsidence is essential for geohazard prevention, ecological restoration, and safe mining. Conventional approaches, however, are limited by the sparse spatial distribution of GNSS observations, the difficulty of InSAR in resolving large and rapidly evolving deformation, and the reliance of probability integral method (PIM) calibration on sparse observations. Here, we evaluate an integrated UAV-LiDAR–PIM workflow for basin-wide characterization of mining-induced subsidence and improved spatial constraint of PIM parameters. We use the 3206 working face in an Ordos mining area as a case study. DEM differencing of multi-temporal UAV-LiDAR observations characterizes the spatial distribution of the subsidence basin. Uniform sampling within the affected zone then provides a high-density dataset for PIM calibration, which we compare with conventional profile-based monitoring. The two UAV-LiDAR DEM epochs yielded vertical quality-control RMSEs of 0.052 and 0.050 m, respectively, with a mean of 0.051 m. These results support their use for basin-scale deformation analysis. The inverted angular parameters indicate a larger mining influence extent along the dip direction than along the strike. Surface movement followed initial, active, and declining stages over 423 days, with a maximum subsidence rate of 87.39 mm d−1 during the active stage. The integrated workflow reduces the spatial-sampling limitations of profile-based monitoring and adds two-dimensional constraints on basin geometry and directional variation. Combining basin-wide UAV-LiDAR observations with conventional profiles may improve site-specific PIM calibration and support mining-hazard assessment and ecological-restoration planning.

Hai-Rui Li, Yao-Jun Zhang, Wen-yuan Zhao et al. · 0 citations
Conference Open access Aug 2026

A Strategic Framework for Integrating Unmanned Aerial Vehicles (UAV)-Based Pavement Assessments into the Egyptian Road Network

Pavement condition assessment is essential for effective road network management, as paved roads deteriorate over time due to traffic loading and environmental effects. Traditional pavement surveys rely on in-situ measurements and visual inspections to identify surface distresses such as cracking, raveling, and weathering. Although widely used, these methods are often labour-intensive, time-consuming, costly, and may disrupt traffic while exposing inspectors to safety risks. Recent advances in unmanned aerial systems (UAS) provide a promising alternative for pavement condition assessment. UAV-based surveys enable rapid data collection over large areas using high-resolution imaging and sensor technologies, which can be integrated with artificial intelligence (AI) techniques for automated pavement distress detection and analysis. In Egypt, the rapid expansion of the road network and increasing maintenance demands highlight the need for an efficient, continuous, and reliable pavement monitoring system. This study presents an Egypt-focused framework that links UAV data acquisition, AI-based distress detection, and PCI-based decision-making to support the integration of UAV-based pavement inspection into existing road management practices. This study supports an Egypt-focused framework for integrating UAV-based pavement inspection into existing road management practices. The proposed framework outlines UAV data acquisition, AI-based distress detection, and pavement condition evaluation workflows, while considering local environmental, operational, and regulatory constraints. The framework is informed by successful international applications and is intended to enable safer, faster, and more cost-effective pavement assessment to support sustainable road network management in Egypt.

Abdel-Halem A. Abdel-hamed, Abdallah Samir Abdallah, Ibrahim Elnaml et al. · 0 citations
Open access Jul 2026

Synergistic Monitoring Framework for Mining Subsidence Under Thick Loose Layers by Integrating InSAR and UAV Photogrammetry

The surface subsidence caused by coal mining is a geological environmental disaster that restricts the sustainable development of mining areas. Traditional monitoring methods have limitations in long-term and high-precision observation. Therefore, this paper proposes a synergistic monitoring framework for mining subsidence under thick loose layers by integrating Interferometric Synthetic Aperture Radar (InSAR) technology and Unmanned Aerial Vehicle (UAV) photogrammetry. The research results show: (1) UAV photogrammetry can accurately obtain the large gradient deformation at the center of the subsidence basin, while InSAR has better accuracy at the basin edge. The proposed fusion method is significantly superior to a single method. (2) The parameters obtained by the probability integral method based on the fused data are in good agreement with the parameters obtained by leveling measurement data, and the relative error of the parameters is less than 4%. (3) The thick and loose-layered mining areas have the characteristics of larger subsidence, steeper gradient at the center, slow convergence at the edge, and wide influence range. This study provides a new approach for precise subsidence monitoring, and the revealed subsidence characteristics provide a scientific basis for disaster assessment and mining optimization in similar areas.

Shu Li, Guang Hu, Tao Zhang et al. · 0 citations
Open access Sep 2026

Surface Subsidence Analysis and Prediction in an Open-Pit Mine Using Time-Series InSAR and a CL-TSF Hybrid Model

Surface subsidence in mining areas is a widespread, long-term, and slow-onset geological hazard that can induce cascading failures, including slope instability and ground collapse, thereby threatening infrastructure and human safety. Accurate characterization of its spatiotemporal evolution and reliable prediction of future trends are therefore essential for effective mine safety management and hazard assessment. This study investigates an open-pit mine by integrating time-series Interferometric Synthetic Aperture Radar (InSAR) monitoring with a deep learning–based prediction framework. A total of 70 Sentinel-1A synthetic aperture radar (SAR) images acquired between January 2023 and May 2025 were processed to quantify surface deformation. The results reveal a large-scale subsidence funnel, with a maximum subsidence rate of 143.00 mm/yr and a cumulative displacement of −337.89 mm. The observed deformation is controlled by combined effects of rainfall, seismic activity, and local geological conditions. To predict the temporal evolution of subsidence, a hybrid convolutional neural network–long short-term memory (CNN–LSTM) time-series forecasting model (CL-TSF) is proposed. By integrating convolutional feature extraction with long short-term memory–based sequence modeling, the model effectively captures spatial patterns and long-term temporal dependencies. Compared with conventional CNN and LSTM models, the proposed approach achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 2.24% and a Root Mean Square Error (RMSE) of 5.260. Its robustness is validated through accurate multi-step prediction of the final five deformation periods. These findings provide insights into mining-induced subsidence mechanisms and demonstrate the potential of the proposed framework for dynamic early warning and risk assessment in mining areas.

Unknown authors · 0 citations
Open access Aug 2026

Research on Key Technologies and Safety Warning of Deep Excavation Support Structures Based on Multi Source Sensing and Real Time Data Fusion

Deep excavation support structures exhibit nonlinear and spatiotemporally coupled deformation under complex geological conditions and construction disturbances, making single-sensor monitoring insufficient for real-time safety warning. This study proposes a multi-source sensing and real-time data fusion method for safety early warning of deep foundation pit support structures. A heterogeneous sensor network composed of strain gauges, inclinometers, axial force gauges, and hydrostatic levels is deployed to monitor pile bending moment, horizontal displacement, support axial force, and surface settlement. Sensor streams with different sampling rates are temporally aligned by cubic spline interpolation and spatially mapped onto a unified support-pile profile. Outliers are removed using the Pauta criterion, and adaptive inverse-variance weighting is combined with Kalman filtering to obtain robust fused feature sequences. An improved grey relational analysis model with exponential time weighting and dynamic thresholds is then used to identify typical failure modes and issue three-level warnings. Field experiments using 30 days of monitoring data show that the method achieves 98.3% effective data retention, 20.3 dB signal-to-noise ratio, RMSE of 0.34 mm, warning response time of 4.2 s, and false alarm rate of 8.6%. The method improves structural sensing reliability and real-time safety warning capability.

Yuxiang Zhang · 0 citations

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