Distributed estimation of surface sediment size in paraglacial and periglacial environments using drone photogrammetry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".