OrthoSAM: Multi-Scale Extension of the Segment Anything Model for River Pebble Delineation from Large Orthophotos

Abstract. Sediment characteristics and grain-size distribution are crucial for understanding natural hazards, hydrologic conditions, and ecosystems. However, traditional methods for collecting this information are costly, labor-intensive, and time-consuming. To address this, we present OrthoSAM, a workflow leveraging the Segment Anything Model (SAM) for automated delineation of densely packed pebbles in high-resolution orthomosaics. Our framework consists of a tiling scheme, improved seed (input) point generation, and a multi-scale resampling scheme. Validation using synthetic images shows high precision close to 1, a recall above 0.9, with a mean IoU above 0.9. Using a large synthetic dataset, we show that the two-sample Kolmogorov-Smirnov test confirms the accuracy of the grain size distribution. We identified a size detection limit of 30 pixels; pebbles with a diameter below this limit are not reliably detected. Applying OrthoSAM to orthomosaics from the Ravi River in India, we delineated 6087 pebbles with high precision (0.93) and recall (0.94). The resulting grain statistics include area, axis lengths, perimeter, RGB statistics, and smoothness measurements, providing valuable insights for further analysis in geomorphology and ecosystem studies.