Distributed estimation of surface sediment size in paraglacial and periglacial environments using drone photogrammetry

Abstract Grain‐size analysis offers insights into geological processes and landform dynamics. Traditional grain‐size sampling methods are labour intensive and offer limited spatial coverage, posing challenges in paraglacial and periglacial environments characterized by large spatial variability in sediment sizes. This study introduces a new workflow that combines structure‐from‐motion, image segmentation and texture‐based optical granulometry techniques to estimate surface grain size in paraglacial and periglacial environments efficiently. Utilizing high‐resolution orthomosaics (ground sampling distance 8 mm) and Cellpose, a deep‐learning image segmentation model, the new workflow achieves high‐accuracy grain‐size distributions (GSDs) with low errors. These GSDs, along with lower resolution orthomosaics (ground sampling distance 30 mm), are used to train SediNet—a machine‐learning framework—to predict GSDs accurately from pixel tiles. Tested across six alpine basins in the Canadian Rockies and a rock glacier in Italy, the model demonstrates effectiveness and accuracy, promising advancements in geoscientific research and the understanding of paraglacial and periglacial dynamics.