Mapping water bodies using SAR imagery - an application over the Spiritwood valley aquifer, Manitoba
Bibliographic record
Abstract
Canada has over 2 million lakes covering a total area of 0.9 million km2. The inland water bodies play a critical role in water cycles, water resources, social economic including fisheries and recreation. However, these inland aquatic ecosystems are under increasing pressure and big changes from increasing human activities and changing climate. To better understand the aquatic ecosystem dynamics and effectively manage the inland water bodies, it is essential to have up-to-date information of their spatial and temporal variability. Synthetic Aperture Radar (SAR), unlike optical sensors, is able to penetrate cloud, haze and smoke, and hence observe the earth's surface in all weather conditions day and night. SAR imagery is an effective method for mapping water bodies. This open file details the algorithms and their implementations for a novel method for mapping water bodies using SAR imageries. This method is completely automatic and less computational intensive, thus suitable for large-scale applications. A test of this method over the Spiritwood valley in Manitoba using Radarsat-2/SAR data shows a high accuracy in delineating water bodies. This study provides a tool for mapping national scale inland water bodies and monitoring their dynamic changes in a near-real time environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".