Large-scale estimation of surficial sediment size in alpine landforms using UAV photogrammetry and machine learning.
Bibliographic record
Abstract
Accurate estimation of surficial sediment size in alpine landforms such as talus slopes, rock glaciers, and moraines is crucial for understanding geomorphologic processes and predicting the potential impact of natural hazards. Traditional methods for measuring sediment size in these environments can be time-consuming and labor-intensive. Additionally, they are usually applied to selected areas and are rarely used to cover larger areas, making the development of more efficient approaches essential. This study presents a new method for estimating large-scale surficial sediment size based on unmanned aerial vehicle (UAV) photogrammetry and combining SediNet and PebbleCountAuto image analysis methods. SediNet is a configurable machine-learning framework for estimating either (or both) continuous and categorical variables from a photographic image of clastic sediment. SediNet can achieve subpixel resolutions because the dimensions of the grains aren't being measured directly. However, site-specific sediment sizes are necessary to train this model. On the other hand, PebbleCountAuto does not require any site calibration by using segmentation methods to delimitate the grains automatically and provide a full grain-size distribution. Our proposed methodology trains the SediNet model using the sediment sizes outputs of the PebbleCountAuto method. Our study area is the upper part of the Lake O'Hara watershed in the Canadian Rockies, composed of talus slopes and a large ice-cored moraine. We performed two types of UAV flights; high-altitude flights (~100 m height) to cover the whole study area with medium-to-high resolution orthomosaic (pixel resolution 3 cm) and low-altitude flights (~25 m height) at smaller patches to achieve high-resolution orthomosaic (pixel resolution 5-8 mm). First, the sediment size was estimated in the high-resolution patches with the PebbleCountAuto method. Then, these results were used to train the SediNet model and generate a large-scale sediment size estimation. Our results show that this combination of methods is a reliable and efficient approach for accurately estimating sediment sizes in alpine landforms. The use of UAV photogrammetry allows for the rapid and cost-effective collection of high-resolution imagery, while the combination of SediNet and PebbleCountAuto provides robust estimates of sediment size over large areas. This new method can improve our understanding of geomorphologic processes and hazard assessment in these environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".