VALIDATION OF SPACEBORNE RADAR SURFACE WATER MAPPING WITH OPTICAL sUAS IMAGES
Bibliographic record
Abstract
Abstract. The Canada Centre for Remote Sensing (CCRS) has over 40 years of experience with airborne and spaceborne sensors and is now starting to use small Unmanned Aerial Systems (sUAS) to validate products from large coverage area sensors and create new methodologies for very high resolution products. Wetlands have several functions including water storage and retention which can reduce flooding and provide continuous flow for hydroelectric generation and irrigation for agriculture. Synthetic Aperture Radar is well suited as a tool for monitoring surface water by supplying acquisitions irrespective of cloud cover or time of day. Wetlands can be subdivided into three classes: open water, flooded vegetation and upland which can vary seasonally with time and water level changes. RADARSAT‐2 data from the Wide-Ultra Fine, Spotlight and Fine Quad-Pol modes has been used to map the open water in the Peace‐Athabasca Delta, Alberta using intensity thresholding. We also use spotlight modes for higher resolution and the fully polarimetric mode (FQ) for polarimetric decomposition. Validation of these products will be done using a low altitude flying sUAS to generate optical georeferenced images. This project provides methodologies which could be used for flood mapping as well as ecological monitoring.
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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 | 0.000 | 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".