From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
This study evaluates Random Forest and four CNNs (ResNet-50, ResNet-101, EfficientNet-B4, ConvNeXt-Large) for transferable satellite-derived bathymetry over 0-20 m depth using Sentinel-2 imagery. Key design choices include preserving spatial continuity (contiguous reef blocks) and a Smooth Weight Function (SWF)-weighted RMSE loss. Intra-regional RMSE ranges from 1.15-1.92 m (as low as 0.26 m for shallow depths), while cross-regional RMSE is 2.46-2.98 m for deep models. On the MagicBathyNet benchmark, the proposed networks achieve 0.19-0.22 m RMSE, outperforming U-Net and a task-specific transformer with fewer parameters. Multi-temporal imagery and median aggregation reduce noise. Optimized architectures and pretrained weights are released for scalable transfer.
[2606.02764] From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
[Submitted on 1 Jun 2026]
Title:From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
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Abstract:Satellite-derived bathymetry (SDB) from multispectral imagery is cost-effective but scales poorly across regions, especially in optically complex coastal environments. We evaluate machine learning and deep learning for transferable SDB over the 0-20 m depth range using Sentinel-2 imagery. A Random Forest baseline and four CNNs (ResNet-50, ResNet-101, EfficientNet-B4, ConvNeXt-Large) are trained on Pratas Island and selected Great Barrier Reef regions, then evaluated on spatially independent intra- and cross-regional test areas. Preserving spatial continuity during training, by keeping contiguous reef blocks rather than random patches, is the single most impactful design choice; we further introduce a Smooth Weight Function (SWF)-weighted RMSE loss that emphasizes near-surface depths. With these choices, intra-regional RMSE ranges from 1.15 to 1.92 m over 0-20 m and is as low as 0.26 m for depths 2.99-3.78 m), while the deep models stay more robust (2.46-2.98 m). On the public MagicBathyNet aerial-RGB benchmark (0-16 m) the proposed networks reach 0.19-0.22 m RMSE, outperforming a U-Net baseline and a task-specific transformer architecture with substantially fewer parameters. We further exploit multi-temporal repeat imagery: training on it broadens diversity, and median-aggregating predictions across passes at inference reduces noise from changing sun angles, atmospheric conditions, water properties, and tides. We release optimized architectures and pretrained weights to enable scalable transfer to new sites.
Comments: 42 pages, 13 figures, 15 tables. Supplementary Information provided as ancillary file (anc/SI.pdf). Code and pretrained weights at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computational Physics (physics.comp-ph)
Cite as: arXiv:2606.02764 [cs.CV]
(or arXiv:2606.02764v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.02764
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Remote Sens. 18 (2026) 1768
Related DOI:
https://doi.org/10.3390/rs18111768
DOI(s) linking to related resources
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From: Joachim Moortgat [view email] [v1] Mon, 1 Jun 2026 18:28:18 UTC (37,526 KB)
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