Arctic coastal erosion: UAV-SfM data collection strategies for planimetric and volumetric measurements
Bibliographic record
Abstract
Above average warming in the Arctic is leading to increasing permafrost temperatures and a reduction in sea ice cover, which are expected to contribute to increasing rates of Arctic coastal erosion and sediment release. We studied a 1.5 km stretch of coastline off Richard’s Island, Northwest Territories, Canada, consisting of multiple retrogressive thaw slumps (RTSs) with varying degrees of activity over a one-year period. Multi-temporal 2D and 3D geomorphic analysis was based on unmanned aerial vehicle-Structure-from-Motion (UAV-SfM) data sets collected in 2018 and 2019. Over the observation period, −3.9 m and −1.1 m of planimetric cliff edge and toe retreat occurred, respectively, and corresponded to an average volumetric change of 8.1 m 3 m −1 . The accuracy of UAV-SfM-derived digital elevation models was tested using 12 data collection and processing scenarios, testing the influence of off-nadir camera angle, flight pattern, and georeferencing strategy. We found that oblique imaging and georeferencing strategy had a large influence on vertical accuracy and variability across the study site and has implications for studying volumetric changes in RTSs. This study furthers the geomorphological understanding of RTS processes by highlighting the complex relationship between planimetric and volumetric change along rapidly retreating Arctic coasts, and demonstrates advancements in measurement practices for UAV-SfM data sets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".