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Volume 13
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10.3390/jmse13071374
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Article

Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation

by
Christos G. E. Anagnostopoulos
1,
Vassilios Papaioannou
1,*,
Konstantinos Vlachos
2,
Anastasia Moumtzidou
1,
Ilias Gialampoukidis
1,
Stefanos Vrochidis
1 and
Ioannis Kompatsiaris
1
1
Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece
2
CDXi Solutions P.C., Filikis Etaireias 12, 54621 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(7), 1374; https://doi.org/10.3390/jmse13071374
Submission received: 13 June 2025 / Revised: 15 July 2025 / Accepted: 17 July 2025 / Published: 18 July 2025
(This article belongs to the Section Physical Oceanography)
Versions Notes

Abstract

Satellite-derived bathymetry (SDB) enables the efficient mapping of shallow waters such as coastal zones but typically requires extensive local ground truth data to achieve high accuracy. This study evaluates the effectiveness of transfer learning in reducing this requirement while keeping estimation accuracy at acceptable levels by adapting a deep learning model pretrained on data from Puck Lagoon (Poland) to a new coastal site in Agia Napa (Cyprus). Leveraging the open MagicBathyNet benchmark dataset and a lightweight U-Net architecture, three scenarios were studied and compared: direct inference to Cyprus, site-specific training in Cyprus, and fine-tuning from Poland to Cyprus with incrementally larger subsets of training data. Results demonstrate that fine-tuning with 15 samples reduces RMSE by over 50% relative to the direct inference baseline. In addition, the domain adaptation approach using 15 samples shows comparable performance to the site-specific model trained on all available data in Cyprus. Depth-stratified error analysis and paired statistical tests confirm that around 15 samples represent a practical lower bound for stable SDB, according to the MagicBathyNet benchmark. The findings of this work provide quantitative evidence on the effectiveness of deploying data-efficient SDB pipelines in settings of limited in situ surveys, as well as a practical lower bound for clear and shallow coastal waters.
Keywords: satellite-derived bathymetry; transfer learning; remote sensing; MagicBathyNet; U-Net; Sentinel-2 satellite-derived bathymetry; transfer learning; remote sensing; MagicBathyNet; U-Net; Sentinel-2

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MDPI and ACS Style

Anagnostopoulos, C.G.E.; Papaioannou, V.; Vlachos, K.; Moumtzidou, A.; Gialampoukidis, I.; Vrochidis, S.; Kompatsiaris, I. Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation. J. Mar. Sci. Eng. 2025, 13, 1374. https://doi.org/10.3390/jmse13071374

AMA Style

Anagnostopoulos CGE, Papaioannou V, Vlachos K, Moumtzidou A, Gialampoukidis I, Vrochidis S, Kompatsiaris I. Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation. Journal of Marine Science and Engineering. 2025; 13(7):1374. https://doi.org/10.3390/jmse13071374

Chicago/Turabian Style

Anagnostopoulos, Christos G. E., Vassilios Papaioannou, Konstantinos Vlachos, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2025. "Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation" Journal of Marine Science and Engineering 13, no. 7: 1374. https://doi.org/10.3390/jmse13071374

APA Style

Anagnostopoulos, C. G. E., Papaioannou, V., Vlachos, K., Moumtzidou, A., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. (2025). Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation. Journal of Marine Science and Engineering, 13(7), 1374. https://doi.org/10.3390/jmse13071374

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