AI Summary & Key Takeaways
AI InsightThe findings show that visualization supported by realistic accuracy values can significantly contribute to the identification of landslide areas that may be caused by excavation and are likely to progress over time in an open pit mine site.
📌 3 Key Takeaways
Abstract
Open pit mining is a common technique for extracting surface mineral resources, but it poses safety risks due to terrain instability and slope movements. Accurate and efficient monitoring of surface changes is essential for operational safety and informed decision-making. Recently, UAV-based photogrammetric point clouds have emerged as a cost-effective and flexible alternative to traditional geodetic methods. Among the analysis tools, Multiscale Model to Model Cloud Comparison (M3C2) stands out for detecting 3D changes between epochs. However, standard M3C2 relies on internal accuracy, which may lead to overly optimistic assessments. This study introduces a visualization-based approach using realistic external-accuracy test statistics derived from GNSS control points. Within the approach, a new visualization metric based on the ratio of the norms of 2D and 3D change vectors is proposed. In cases where there is a predominant movement in the horizontal direction, the calculated ratio values are larger compared to their immediate surroundings. In this way, these locations become more perceptible and user attention is directed to the highlighted locations. Thus, it will be possible especially for non-specialized users to better examine these prominent locations in the images and in the field when necessary. In this way, decision makers will be provided with a practical tool to alert the field personnel working in the relevant locations and to ensure security quickly. The findings show that visualization supported by realistic accuracy values can significantly contribute to the identification of landslide areas that may be caused by excavation and are likely to progress over time in an open pit mine site.