UAV-SfM and Geographic Object-Based Image Analysis for Measuring Multi-Temporal Planimetric and Volumetric Erosion of Arctic Coasts
Bibliographic record
Abstract
Monitoring and quantifying the rapid changes along Arctic coasts is becoming increasingly important as above average warming in the Arctic is contributing to increasing rates of erosion leading to dramatic impacts on coastal ecosystems and communities. Understanding the impacts of Arctic coastal erosion on the climate system across large coastal scales requires improvements in measurement techniques. We analyzed two coastal sites in Kugmallit Bay (near Tuktoyaktuk, Northwest Territories, Canada), over a one-week and one-year time interval. Using high-resolution imagery from Unoccupied Aerial Vehicles with Structure from Motion (UAV-SfM), we investigated the influence of unique coastal indicator features on reported planimetric and volumetric measurements and explored the use of Geographic Object Based Image Analysis (GEOBIA) to semi-automate the process of coastal feature extraction. We observed temporally dependent differences between coastal feature movements, planimetrically and volumetrically, and object-based feature extraction accuracy was found to be feature dependent. Our research has made methodological improvements to Arctic coastal measurements, particularly at high spatiotemporal scales, which highlights considerations relevant to broad scale Arctic coastal monitoring and quantification.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".