Automated Particle Size Distribution Estimation of Rock Avalanches using Deep Learning

Accurate estimation of Particle Size Distribution (PSD) in rock avalanche deposits is essential for understanding the fragmentation processes and spatial distribution characteristics during mass movement. However, traditional methods, such as physical sieving or visual field estimation, are time-consuming, labor-intensive, and impractical for large-scale field measurements. To address these limitations, this study presents an automated PSD estimation framework that combines UAV imagery and deep learning-based segmentation. A synthetic dataset was used to train the segmentation model, improving its robustness across different scenarios. Image resolution adjustments were applied to improve detection accuracy for small and overlapping particles. Additionally, Fourier analysis was utilized to reconstruct smooth and continuous particle contours, to effectively handle overlapping particles. The reconstructed 2D outlines were further used to estimate 3D particle volumes through the shape-volume model based on laboratory and literature data. Projection correction was applied to mitigate image distortions to ensure precise volume predictions. The proposed approach overcomes the limitations of traditional methods dealing with complex particle distributions in real field environments. The results demonstrate the effectiveness of the proposed method for large-scale particle detection and volume estimation, providing new insights into rock avalanche fragmentation dynamics.

Keywords: Rock avalanche; Particle size distribution (PSD); deep learning; UAV Imagery