https://doi.org/10.1002/esp.70093

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 340 340 pixel tiles. Tested across six alpine basins in the Canadian Rockies and a rock glacier in Italy, the model demonstrates effectiveness and accu- racy, promising advancements in geoscientific research and the understanding of paraglacial and periglacial dynamics.

https://doi.org/10.1002/esp.70093