A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques.
Bibliographic record
Abstract
The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, \nevaluating its spatial extent interests the authorities responsible for the management of water resources, especially \nhydropower companies. The main objective of this study is the development of a snow-cover mapping strategy \nusing remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational \nmode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be \nsnow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed \nto build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine \nthe outputs of individual classifiers that make up the ensemble in such a way that correct decisions are ampli- \nfied, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to \nsnow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using \nNOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination \nof six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover \nmapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the \nother for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions \nof the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the \nensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes \nthe pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is \ncompared to the overall performance of the chosen classifier, and also with ground observations at meteorological \nstations.
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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 | 0.002 |
| 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 | 0.000 |
| 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 | 0.000 |
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".