Evaluation of SfM for surface characterization of a snow-covered glacier through comparison with aerial lidar
Bibliographic record
Abstract
The combined use of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) is rapidly growing as a cost-effective alternative to airborne laser scanning (lidar) for reconstructing glacier surfaces. Here we present a thorough analysis of the precision and accuracy of a photogrammetric point cloud (PPC) constructed through SfM from UAV-acquired imagery over the spring snow surface at Haig Glacier, Alberta, Canada, the first of its kind in a glaciological setting. An aerial lidar survey conducted concurrently with UAV surveys was used to examine spatial patterns in the PPC accuracy. We found a median error in the PPC of −0.046 ± 0.067 m, with a 95% quantile of 0.218 m. Mean precision of the PPC was 0.199 m, with large spatially clustered outliers. We found an association between high-error, low-precision, and high-surface roughness in the PPC, likely due to illumination characteristics of the snow surface. Glacier surface reconstructions are important for geodetic mass balance measurements, giving key insights into changing climate where in situ measurements are difficult to obtain. The PPC errors are small enough that they would have minimal effects on total mass balance, should the technique be applied across the glacier.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".