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Editorial: Remote sensing applications in oceanography with deep learning
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DOI：10.3389/fmars.2025.1701125.png)
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Abstract
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Deep Learning and Remote Sensing for the Ocean: From Concept to Operational Impact Deep learning (DL) and remote sensing (RS) are transforming how we observe and manage the ocean. Modern algorithms; platforms; and multi-sensor data integration now deliver insights at scales and speeds that were impossible just a few years ago. This Research Topic gathers 17 contributions across seafloor geomorphology; Ship and hazard monitoring; water quality assessment; mesoscale dynamics; under-ice processes; sonar perception; and enabling methods—demonstrating a field that is both technically innovative and mission-driven. Seafloor to Shoreline Ocean science relies on accurate mapping of the seabed. Automation can quickly analyse broad regions while collecting characteristics that satellite altimetry misses; as demonstrated by a CNN + U-Net pipeline for recognising tiny seamounts in multibeam data. RipFinder; a mobile machine learning system for real-time rip current identification that also functions as a citizen-science tool in places with restricted connection; exemplifies "AI to edge" at the land–sea interface. Ships; Safety; and Hazards For marine awareness; synthetic aperture radar (SAR); is still essential. While previous research use AIS data; sea fog; and remote sensing to evaluate collision risk; a super-resolution Mask R-CNN architecture uses scale-aware fusion to improve ship detection in noisy SAR settings; providing evidence-based navigation management tools. Ecosystems and Water Quality Chlorophyll-a (Chl-a) variations and harmful blooms are important ecological markers. Green tide identification from MODIS images is enhanced by WaveNet (VGG16 + BiFPN + CBAM). The importance of physics-aware features is demonstrated by ResUNet models that relate ocean-atmosphere dynamics to Chl-a in the South China Sea. Long-term variability in the Persian Gulf and Arabian Sea is revealed by rebuilt MODIS datasets; and new techniques also yield transferable Chl-a products for estuaries. MarGEN; a GAN-based augmentation technique that enhances marine mammal call categorisation in situations where labelled audio is limited; is one example of an advancement in acoustic ecology. Mesoscale and Cryosphere Dynamics OIEDNet generates the first large-scale MIZ eddy catalogues by detecting under-ice eddies from Sentinel-1 dual-pol data; whereas Conv-LSTM GAN hybrids predict mesoscale eddy properties with high fidelity. Perception Underwater Sonar and visual sensing are crucial for autonomous systems. Forward-looking sonar object detection is improved by MLFANet; side-scan sonar small-object recognition is improved by SOCA-YOLO; and underwater optical imaging is improved by CUG-UIEF using edge-and attention-based fusion. Data; Platforms; and Decision Support New contributions also address scalable data management (LSH-based retrieval for ocean archives) and decision-making (multi-criteria approaches for underwater IoT and AUV deployments); underscoring the need to co-design sensing; connectivity; and computation. Cross-cutting Lessons Five themes emerge: (1) multi-scale architectures consistently boost detectability; (2) embedding physics-aware features enhances generalization; (3) translating models to edge-deployable tools enables real-world impact; (4) data efficiency strategies such as augmentation and self-supervision are critical in data-sparse regimes; and (5) benchmarking and openness will accelerate progress. Outlook This collection highlights a decisive shift from proof-of-concept to operational potential in ocean AI. Future priorities include embedding physical priors; advancing generative/self-supervised methods for sparse data; and ensuring scalability; efficiency; and usability for real-world applications. Together; these works show how DL and RS can protect mariners; monitor ecosystems; and reveal ocean dynamics— bringing us closer to truly actionable ocean intelligence.
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