Advancing Arctic coastal erosion measurement and monitoring through UAV-SfM, satellite imagery, and object-based image analysis
Bibliographic record
Abstract
Arctic coasts are vast, representing 30-34% of Earth’s coastline and exhibit some of the highest rates of erosion in the World due to the presence of permafrost. Rates of erosion are expected to increase with warming air and water temperatures, reductions in Arctic sea ice extent and duration, sea level rise, and increased storm severity and frequency. Erosion of Arctic coasts can lead to rapid land loss threatening habitat, archaeologically significant sites, modern infrastructure, and communities. Rapid erosion and permafrost degradation also leads to the liberation of previously frozen sediment and organic carbon into the nearshore zone which affects marine ecosystems and contributes to ocean acidification. Further, the release of organic carbon from frozen sediment contribute to global greenhouse gas release which are not well understood nor included in current Earth System Models. This thesis focuses on the use of emerging technologies to further our understanding of Arctic coastal processes, specifically, volumetric erosion, and broad scale delineation of multiple shoreline proxies for monitoring and quantification of erosion. UAV-SfM provides aerial and DSM imagery at unprecedented spatial and temporal resolution that provides perspectives and quantitative measures that are unachievable using conventional methods and very high resolution satellite imagery enables broader scale multiple proxy analysis while image classifications derived from OBIA, or GEOBIA, provide opportunities to systematically create boundary features (i.e. shoreline proxies). Overall, this doctoral research develops and evaluates techniques that enhance our ability to make quantitative measures of Arctic coastal erosion, both planimetric and volumetric, that have implications locally, regionally, and globally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".