Global Validation of SWOT River Widths Using Deep Learning Water Masking of PlanetScope Imagery
Bibliographic record
Abstract
The SWOT satellite mission provides unprecedented hydrological measurements of water surface elevation and slope, and inundation extent of rivers globally. While water surface elevation and slope have been validated extensively, inundation extent and width have relied heavily on manually digitized water surfaces from aerial orthophotos, where labeling takes several hours per image, and thus, there are fewer samples to validate. To address this, our team developed a deep learning algorithm (RiverScope) that masks surface water in PlanetScope images. The algorithm was trained on 1,145 manually labeled PlanetScope images of rivers across the globe and the model was applied to masking a global sample of ~5000 SWORD reaches. Each reach has coincident SWOT and PlanetScope data within +12 hours of each other from April 1, 2023 – April 1, 2025, covering a total area of ~ 30 million square kilometers. Using the RiverScope masked images, we calculated effective width for each reach through time, using the SWORD reach length and area of water pixels. Preliminary results, using ~300 reaches within the Yukon River Basin, show that SWOT-derived widths from the RiverSP product have a mean average error (MAE) of 48% and 16% for RiverScope. This presentation will build on the preliminary results, highlighting the range of performance observed across the global sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".