Super-Resolution for Enhanced Fluvial Sediment Measurement in UAV Images 
Bibliographic record
Abstract
Drone imagery can efficiently perform large-scale riverbed grain size measurements. However, its applicability is significantly constrained by image resolution limitations. This issue is especially critical in mountainous areas, where sediments exhibit a wide range of grain sizes and spatial heterogeneity. To address this issue, this paper develops a new fluvial sediment measurement technique for UAV images using a deep learning technique super-resolution (SR). We first used RTK-based UAV technology to collect high-resolution riverbed grain orthophotos of different types of mountain rivers, with the collected UAV images having a resolution between 3~5 mm/pixel. Four types of super-resolution models Nearest Neighbor, Lanczos filter, SRCNN and SRGAN were trained to restore the high-resolution images from low-resolution riverbed images. Three automated grain sizing methods BASEGRAIN, GrainID and ImageGrains were applied to the images restored by SR models, and 113,456 manual grain labels are created as grain size baseline for model evaluation. The efficacy of all three models diminishes with decreasing resolution, with BASEGRAIN being the most robust and GrainID the most sensitive. Application of all four SR models model significantly increase the efficacy of grain size measurement, and SRGAN models with upscaling factor of 4 (SRGAN×4) outperform other models. Further analysis shows the minimum detectable sediment particle size of SRGAN×4 is 1 pixel, which exceed the minimal human vision limitation for detecting grain size. The SR technology proposed in this paper makes it more feasible to rapidly obtain the riverbed grain size over a wide range in mountainous rivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".