Toward Automated Ice-Water Classification on Large Northern Lakes Using RADARSAT-2 Synthetic Aperture Radar Imagery
Bibliographic record
Abstract
Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability. These changes are expected to have implications for both human and environmental systems. Additionally, monitoring lake ice cover is required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently the Canadian Ice Service (CIS) monitors lakes using RADARSAT-2 SAR (synthetic aperture radar) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. \nIn this research, the Iterative Region Growing using Semantics (IRGS) approach has been employed to perform ice-water classification on 61 RADARSAT-2 scenes of Great Bear Lake and Great Slave Lake over a three year period. This approach first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. These classes are manually labelled by the user, however automated labelling capability is currently in development. An accuracy assessment has been performed on the classification results, comparing outcomes with user-generated reference data as well as the CIS fraction reported at the time of image acquisition. The overall average accuracy of the IRGS method for this dataset is 92%, demonstrating the potential of this semi-automated method to provide detailed and reliable lake ice cover information.
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 | 0.000 | 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) | 0.002 | 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 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".