Machine and deep learning methods for satellite-derived bathymetric mapping in Canadian coastal waters
Notice bibliographique
Résumé
The performance of machine and deep learning (ML/DL) classification models is evaluated for depth range mapping in shallow freshwater and saltwater coastal environments in Canada using satellite-derived bathymetry (SDB). The models were trained on data from several Canadian sites, and their transferability was tested on geographically distinct, unseen sites. When compared to depth ranges derived from empirical and physics methods, the DL models, including U-Net, SegNet, and DeepLabv3+ achieved more than twice the F1 scores of in saltwater and freshwater environments, such as Graham Island and Athol, with more modest improvements observed in mixed or complex water types, such as Rimouski. From the class-wise scores, ML/DL models can moderately predict depth ranges up to 8 m in freshwater due to greater water transparency, and up to 3 m depth ranges in saltwater, where suspended sediments limit light penetration. Traditional methods struggled to derived water depth classes in deeper and turbid waters but performed similarly to the machine learning Random Forest classifier model in both environments. In addition to the classification performance metrics (precision, recall, F1), visual assessment of the predicted bathymetric maps showed that DL models captured shoreline features, water depth gradients, and seafloor morphology more accurately, especially in shallow waters. In general, the ML/DL models faced challenges in unseen geographic domains in the 2-8 m water depth classes. Overall, this study highlights the potential of machine learning and deep learning over traditional methods while also emphasizing the need for diverse training data improvements in model transferability. • Satellite-derived bathymetry (SDB) modelling is a relatively novel technique for surveying shallow coastal zones. It involves passive remote sensing techniques to estimate water depth from satellite imagery. According to our literature research, there is ongoing research in SDB using data-driven models in tropical and subtropical regions; however, there is limited work in higher-latitude regions beyond a latitude of 35°. • This study evaluates the performance of machine and deep learning (ML/DL) models for satellite-derived bathymetry (SDB) compared to traditional empirical and physics-based methods in high-latitude freshwater and saltwater coastal environments in Canada. • DL models, including U-Net, SegNet, and DeepLabv3+, achieved approximately twice the F1 scores of traditional methods. • The depth results met the Zones of Confidence (ZOC) categories, CATZOC C and CATZOC D, of the International Hydrographic Organization (IHO) depth accuracy standards. • Overall, this study highlights the advantages of machine learning and deep learning over traditional methods while identifying challenges in model generalization and data diversity.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».