Open-pit slope stability is one of the key factors determining the safety and efficiency of open-pit mining operations. With increasing pit depth, growing complexity of rock mass structure, and intensification of mining activities, the need for advanced methods for studying slope stability based on digital technologies is steadily increasing. The aim of this study is to systematize and comparatively analyze modern methods for investigating open-pit slope stability, taking into account their capabilities and limitations under mining conditions. The paper presents an analytical review of computational geomechanical approaches, geodetic instrumental methods, terrestrial laser scanning, UAV-based photogrammetry, and satellite radar interferometry (InSAR). A comparative analysis of these methods is performed based on key parameters, including data type, spatial coverage, and capabilities for deformation analysis. The results show that each method has specific advantages and limitations related to data characteristics, observation conditions, and analytical potential. It is established that none of the methods, when used individually, provides a complete and reliable description of slope stability. The highest level of informativeness is achieved through the integration of different methods within comprehensive analytical approaches. The results of this study provide a systematic understanding of the role of modern digital methods in slope stability research and confirm the перспективность of developing integrated digital systems for monitoring and assessing open-pit slope stability.

Relevance. Tailing storage facilities (TSFs) are among the most accident-prone hydraulic structures in the mining industry, with a reported average failure-rate estimate of approximately 1.2%, while independent cumulative estimates range from 1.2% to 4.4% depending on the assumed global TSF inventory—nearly 120 times higher than that of traditional large reservoir facilities. Collapse of such structures is accompanied by catastrophic environmental and socioeconomic consequences, which underscore the need to improve monitoring and early-warning systems. Goal. A systematic analysis of current technologies for monitoring deformations in ground dams of TSFs, a quantitative assessment of their capabilities and limitations, and development of a conceptual architecture for an integrated geospatial monitoring system. Methods. A systematic review was conducted for 129 publications (2017–2026) discussing monitoring methods (GNSS, InSAR, SBAS, PS-InSAR, GB-InSAR, UAV, LiDAR, geotechnical sensors, geophysics), data integration technologies, AI/ML forecasting, digital twins, and early-warning systems. Results. It has been determined that no isolated method on its own provides complete monitoring of the structure’s condition: GNSS data are localized; InSAR loses coherence when deformations exceed 15 cm/year; UAVs are weather-dependent; and geotechnical sensors are point-based. Individual studies report reductions in estimation uncertainty of approximately 20–50% when complementary monitoring sources are integrated. Individual studies also report reductions in EWS response time and extended forecast lead times, but these values are heterogeneous and are not pooled in the present review. In one case study, a hybrid FEM–ANN–LSTM model achieved a mean absolute error (MAE) of 0.027 for forecasting the dimensionless factor of safety (FoS) compared with 0.081 for an SVM baseline. Key research gaps have been identified: lack of explainable AI, limited validation of digital twins, absence of standardization in data fusion, and the lack of integration of climate risks. Conclusions. Further improvements in TSF safety require a shift from a set of independent monitoring methods to an integrated geospatial system that combines satellite, ground-based, geotechnical, and geophysical data with artificial intelligence, digital twins, and smart decision support systems. An eight-level architecture for such a system has been proposed, along with three implementation scenarios (minimal, comprehensive, and intelligent) and a roadmap through 2035.