Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation
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 estimatio...
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MDPI AG
2025-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1374 |
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| author | Christos G. E. Anagnostopoulos Vassilios Papaioannou Konstantinos Vlachos Anastasia Moumtzidou Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris |
| author_facet | Christos G. E. Anagnostopoulos Vassilios Papaioannou Konstantinos Vlachos Anastasia Moumtzidou Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris |
| author_sort | Christos G. E. Anagnostopoulos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8fe54778935d4c90a3f93873cfa485a5 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-8fe54778935d4c90a3f93873cfa485a52025-08-20T03:36:11ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-07-01137137410.3390/jmse13071374Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain AdaptationChristos G. E. Anagnostopoulos0Vassilios Papaioannou1Konstantinos Vlachos2Anastasia Moumtzidou3Ilias Gialampoukidis4Stefanos Vrochidis5Ioannis Kompatsiaris6Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceCDXi Solutions P.C., Filikis Etaireias 12, 54621 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceInformation Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceSatellite-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.https://www.mdpi.com/2077-1312/13/7/1374satellite-derived bathymetrytransfer learningremote sensingMagicBathyNetU-NetSentinel-2 |
| spellingShingle | Christos G. E. Anagnostopoulos Vassilios Papaioannou Konstantinos Vlachos Anastasia Moumtzidou Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation Journal of Marine Science and Engineering satellite-derived bathymetry transfer learning remote sensing MagicBathyNet U-Net Sentinel-2 |
| title | Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation |
| title_full | Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation |
| title_fullStr | Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation |
| title_full_unstemmed | Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation |
| title_short | Sentinel-2 Satellite-Derived Bathymetry with Data-Efficient Domain Adaptation |
| title_sort | sentinel 2 satellite derived bathymetry with data efficient domain adaptation |
| topic | satellite-derived bathymetry transfer learning remote sensing MagicBathyNet U-Net Sentinel-2 |
| url | https://www.mdpi.com/2077-1312/13/7/1374 |
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