Sea-ice topographic surveying using Structure-from-Motion photogrammetry conducted from small UAVs
Bibliographic record
Abstract
The heterogenous topography of sea-ice is difficult to measure, monitor and predict.Recent technological improvements have enabled the development of structure from motion (SfM) surveying using small unmanned aerial vehicles (sUAVs).sUAV-SfM surveying was evaluated as a low-cost technique of obtaining sea-ice topography.Field data collection was conducted in Frobisher Bay and consisted of several sUAV-SfM surveys at the sub-kilometre-and kilometre-level spatial scale.RMSE values of 87 mm and 80 mm were obtained, respectively, for comparing the sUAV-SfM to a reference dataset and for comparing among individual sUAV-SfM surveys.The technique was successful at detecting, measuring and visualizing sea-ice features, such as pressure ridges.When compared to established techniques at similar spatial scales, sUAV-SfM was generally superior in terms of cost, simplicity, flexibility and ground resolution, but it suffered from low reliability due to accuracy issues with low-cost GPS receivers.I'll be forever grateful for all the support I've received from multiple people and organizations throughout this thesis.Thanks to my supervisors, Derek Mueller and Adrienne Tivy, who proposed the unexpectedly rich subject of sea-ice as a target to explore SfM, for your guidance during the preparation and post-processing, and for your invaluable support during the thesis writing.Thanks to Adam Garbo, since without your tremendous help, your UAV and fieldwork expertise and the hundreds of hours you've dedicated, both this thesis and the UAV would have had never taken-off.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".