The Synergistic Use of RADARSAT-2 Ascending and Descending Images to Improve Surface Water Detection Accuracy in Alberta, Canada
Bibliographic record
Abstract
Large, e.g., provincial or national, scale near-real-time surface water monitoring is an ambitious task, which can be accomplished by using Synthetic Aperture Radar (SAR) satellite data. SAR has demonstrated the ability to distinguish water and land, but there are many common errors of commission and omission that arise due to the side-looking nature of SAR and due to some landcover types with similar backscatter like roads and pasture. A method is proposed to fix/mitigate these errors through the use of combined ascending/descending RADARSAT-2 image pairs and ancillary data. The results of a corrected water/land binary image were, on average, 99.4% accurate for the Boreal Forest Region (Utikuma) of Alberta, Canada, while for the Rocky Mountain Region (Westcastle) also in Alberta, the results proved to be 99.9% accurate when distinguishing water from land. These accuracies were achieved through the reduction of the water false positive rate and a slight reduction in the water true positive rate. These high accuracy values can be partially attributed to the relative low ratios of water to land in the study regions. We hope that these methods can be used and improved in order to move towards large scale dynamic surface water and wetland mapping.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".