Viola-Joanna Stamer, Panagiotis Agrafiotis, Behnood Rasti, Begüm Demir
4 min
Abstract
Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem monitoring. However, ocean remote sensing (RS) remains constrained by limited labeled data and by the reduced transferability of models pre-trained mainly on land-dominated Earth observation imagery. In this paper, we propose OceanMAE, an ocean-specific masked autoencoder that extends standard MAE pre-training by integrating multispectral Sentinel-2 observations with physically meaningful ocean descriptors during self-supervised learning. By incorporating these auxiliary ocean features, OceanMAE is designed to learn more informative and ocean-aware latent representations from large- scale unlabeled data. To transfer these representations to downstream applications, we further employ a modified UNet-based framework for marine segmentation and bathymetry estimation. Pre-trained on the Hydro dataset, OceanMAE is evaluated on MADOS and MARIDA for marine pollutant and debris segmentation, and on MagicBathyNet for bathymetry regression. The experiments show that OceanMAE yields the strongest gains on marine segmentation, while bathymetry benefits are competitive and task-dependent. In addition, an ablation against a standard MAE on MARIDA indicates that incorporating auxiliary ocean descriptors during pre-training improves downstream segmentation quality. These findings highlight the value of physically informed and domain-aligned self-supervised pre- training for ocean RS. Code and weights are publicly available at https://git.tu-berlin.de/joanna.stamer/SSLORS2.
Alex: Fusion at the bottleneck balances details with big-picture smarts?
Sam: Yes. On oil spills, their best setup beat the prior top model by a clear margin in boundary accuracy.
Alex: And it works much better than land-biased models on balanced measures. Does it hold for all ocean tasks?
Sam: Gains are strongest for picking out debris or pollutants, where sorting water from objects matters. For depth estimation, it stays competitive, but results vary by water type and data amount. Benefits depend on the task and labels available.
Alex: Makes sense with scarce labels. Proof those physical add-ons really drive the gains?
Sam: On debris data, a plain MAE got lower accuracy separating trash from sea clutter, while OceanMAE improved notably. The boost is clear but metric-specific.
Alex: Physically informed training lets it read water like a marine scientist. No more seeing ocean as noisy land.
Sam: Exactly. Trained on 100,000 unlabeled patches worldwide, it builds ocean-specific smarts to handle label shortages. Freezing the codes works well for segmentation. This sets up reliable monitoring without constant relabeling.
Alex: A solid step for actionable ocean insights from satellites. Thanks, Sam—always clears the waves.
Sam: My pleasure, Alex.