River ice tracking and velocity estimation using remote sensing method and numerical modelling
Bibliographic record
Abstract
RÉSUMÉ: RÉSUMÉ:En raison du temps très froid au Canada, les rivières gèlent pendant les saisons froides et des banquises apparaissent à la surface de la rivière, ce qui modifiera les principes hydrauliques de la rivière. Les rivières gelées affectent considérablement l'environnement et l'économie, par exemple en endommageant les infrastructures hydrauliques, et elles causent des pertes financières de millions de dollars par an. Un paramètre clé pour déterminer le rôle des banquises dans l'hydrodynamique des rivières est leur vitesse. La quantification du mouvement de ces banquises peut fournir une opportunité unique d'estimer la vitesse d'écoulement des rivières pour compléter ou remplacer les mesures in situ, qui sont coûteuses et souvent difficiles, en particulier en présence de glace. Par conséquent, pour quantifier le mouvement des banquises, des images satellites quasi-simultanées peuvent être utilisées. Les données d'imagerie satellitaire ont reçu beaucoup d'attention dans différentes spécialités ces dernières années. La technique de télédétection s'est avérée utile pour détecter les banquises à la surface de la rivière. Le déplacement des banquises peut être fourni en les suivant dans des images satellites quasi simultanées. Ici, nous analysons des paires d'images quasi simultanées co-enregistrées à partir d'acquisitions stéréo du capteur satellite GEOEYE-1 avec une résolution d'imagerie spatiale de 0,5 m obtenue auprès de la société Maxar. Un algorithme de corrélation d'images est nécessaire pour suivre les banquises dans deux images séquentielles. Nous utilisons le logiciel Correlation Image Analysis (CIAS) pour quantifier les déplacements des banquises dans la section inférieure de la rivière Athabasca (LAR) en Alberta, au Canada, le 11 Novembre 2009. Ici, en supposant que les champs de vitesse de la glace et de l'eau sont similaires, nous estimer la vitesse de la rivière pour différentes sections transversales de la rivière. ABSTRACT: Due to the severe cold weather in Canada, rivers experience freezing in cold seasons, and ice floes appear on the river's surface, which will change the hydraulic principles of the river. Frozen rivers significantly affect the environment and the economy, for instance, by damaging the hydraulic infrastructures, and they cause financial loss of millions of dollars yearly. A key parameter to determine the role of ice floes in river hydrodynamics is their velocity. Quantifying the movement of these ice floes can provide a unique opportunity to estimate river flow velocity to complete or replace in-situ measurements, which are costly and often challenging, especially in the presence of ice. Therefore, to quantify the movement of ice floes, near-simultaneous satellite images can be used. Satellite imagery data have received much attention in different specialties in recent years. The Remote-Sensing technique has been found to be useful in detecting ice floes on the river's surface. The displacement of ice floes can be provided by tracking them in near-simultaneous satellite images. Here, we analyze co-registered near-simultaneous image pairs from stereo acquisitions of the GEOEYE-1 satellite sensor with a spatial imaging resolution of 0.5 m obtained from the Maxar Company. An image correlation algorithm is needed to track ice floes in two sequential images. We use the Correlation Image Analysis software (CIAS) to quantify the ice floes' displacements in the Lower Athabasca River (LAR) section in Alberta, Canada on 11 November 2009. Here, by assuming that the ice and water velocity fields are similar, we estimate the river velocity for different river cross-sections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".