## 4 Discussion  
The conducted study confirms that the use of photogrammetry supported by unmanned aerial vehicles (UAVs) significantly improves the accuracy of acquiring geometric data for blast design and contributes to enhanced safety by reducing the risk of excessive flyrock. These findings are consistent with the general trend in surface mining, where remote sensing technologies and UAV-based measurement systems are increasingly applied to support operational planning. [PERSON] and [PERSON] [56], in their comprehensive literature review, highlighted the growing importance of UAVs across key mining activities, including drill and blast design. In the presented case study carried out at a basalt quarry in Lower Silesia (Poland), UAV-supported methods enabled the creation of a highly detailed 3D model of the bench face and the generation of centimeter-precision burden heatmaps. This allowed the identification of zones with inadequate burden (<2.5 m) and excessive burden (>4.5 m), which is critical both in terms of flyrock risk and blasting efficiency. Compared to conventional 2D measurements, the 3D analysis revealed burden discrepancies exceeding 100%, particularly in toe zones, which underscores the insufficiency of flat projections for safe blast pattern design. The reduction in the number of required images for 3D modeling by nearly a factor of ten, along with an 80% decrease in processing time, confirmed the operational efficiency of this approach. Moreover, the average reduction of flyrock range by an average of 42% (the average value was estimated based on photographic and video documentation) in areas near protected structures demonstrated the practical effectiveness of blast optimization using 3D spatial analysis. However, certain limitations of the proposed methodology should be acknowledged. UAV-based photogrammetry is sensitive to environmental factors, such as wind, precipitation, fog, lighting conditions, surface reflectivity, and visibility of ground control points, all of which can affect data quality. Additionally, UAV operations require operator certification and are subject to legal regulations, including the obligation to hold valid liability insurance in Poland starting November 2025 [60]. It is important to note that this study did not incorporate predictive modeling methods, such as AdaBoost, random forest, or artificial neural networks, which have been shown to provide highly accurate forecasts of flyrock extent. These techniques allow probabilistic estimation of hazardous areas based on multiple input variables, such as burden, spacing, powder factor, and hole deviation. Combining such models with 3D measurements could further improve prediction accuracy. However, the performance of machine learning models is heavily dependent on the quality and representativeness of the training data, which may be limited in field practice. A review of the current scientific literature clearly shows a growing trend in the use of advanced mathematical and computational models for predicting flyrock throw in blasting operations. In many studies, the reported coefficients of determination (R\({}^{2}\)) exceed 0.95, indicating an almost perfect fit between predicted and observed values. Such high accuracy, however, raises valid concerns from a practical standpoint, considering the significant variability and unpredictability of geological conditions in real-world mining operations. Rock formations differ not only in lithology, but also in bedding, weathering degree, presence of joints, and voids, all of which critically affect the propagation of detonation waves and rock fragmentation. Another methodological concern relates to how the influence of different design parameters is interpreted. Some studies suggest that the powder factor is the most influential parameter in predicting flyrock distance, while burden has a negligible impact.