Assessing RADARSAT-2 for Mapping Shoreline Cleanup and Assessment Technique (SCAT) Classes in the Canadian Arctic
Bibliographic record
Abstract
Continued development of natural resources and a greater human presence in the Arctic places biologically and culturally sensitive shorelines at greater risk of environmental emergencies. Maps of vegetation, substrate, and water classes are needed in order to develop emergency response contingency plans. This study was completed as part of Environment Canada's Emergency Spatial Pre-SCAT for Arctic Coastal Ecosystems (e-SPACE) project, and focused on assessing the potential for improved mapping efficiency through semiautomated classification of the thematic classes defined for these purposes by Environment Canada–Environmental Emergencies Branch, using RADARSAT-2 and SPOT-4 alone and in combination. Repeatability and optimal RADARSAT-2 acquisition parameters were evaluated through comparison of multiple incidence angle images acquired over Tuktoyaktuk Harbour and West Point on Richards Island, Northwest Territories, Canada. Shallow incidence angles (45.3°–49.5°) were preferred over medium (39.3°–41.6°) and steep incidence angles (20.9°–24.2°), and when used alone as inputs, a number of land cover types could be discriminated, including sand, mixed sediment, herbs, shrubs, and wetlands. When used in combination with SPOT-4 data, an overall accuracy of 86.1% and 76.2% was achieved for Tuktoyaktuk Harbour and West Point, respectively, on Richards Island.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".