NEURAL NETWORK-BASED DETERMINATION OF COARSE-GRAINED MATERIAL SIZE IN DEBRIS-FLOW DEPOSITS OF THE SOUTHERN BAIKAL REGION
Journal: Geodynamics & Tectonophysics
Year: 2025

This study is devoted to automation of the assessment of the dimensional structure of coarse-grained material on orthophotomaps obtained from unmanned aerial vehicles (UAVs). Orthomosaics with a Ground Sample Distance (GSD) of ~2–4 cm were used on the material of ten debris-flow deposits of the Baikal region; an annotated dataset was prepared in SedimentAlyzer. The algorithm combines object detection by computer vision methods with subsequent classification and Feret diameter estimation using co