## 6 Conclusions and Future Perspectives
### Summary  
This review provides a comprehensive analysis of the impacts of climate change on oceans and explores potential ocean-based solutions. It identifies key oceanographic tasks and examines how DL has been applied to each, highlighting commonly used architectures that demonstrate strong performance across various applications. However, it should be noted that since different studies utilize diverse evaluation metrics and training and validation datasets, it is difficult to draw definitive conclusions about which architecture performs the best. Despite the challenges, specific DL architectures have shown good performance for certain problems. For example, in SST, MHW, and ENSO prediction tasks, spatiotemporal forecasting models have demonstrated strong performance. U-Net-LSTM has achieved RMSE values ranging from 0.48 \({}^{\circ}\)C to 0.63 \({}^{\circ}\)C across various forecast lead times. Transformer-based architectures, such as Spatial-Temporal Transformer Networks, have also shown promising results in long-range ENSO forecasting, maintaining reliable predictions up to 18 months. For ocean pH estimation, MLPs are often used due to the structured, tabular nature of the input data (e.g., SST, SSS, DIC, pCO2). Thesemodels achieve high accuracy, with RMSEs below 0.01. For sea level prediction, temporal models such as LSTM, BiGRU, and CNN-LSTM are widely used, showing RMSEs around 0.1 m. For sea ice detection and classification, CNN-based models, including U-Net and attention-enhanced models (e.g., DAU-Net), demonstrate excellent performance, achieving accuracies of around 95%. For coral reef, seaweed, and coastal wetland monitoring tasks, DL architectures for image classification and segmentation have shown strong performance. U-Net and its variants (e.g., Dense U-Net, AttU-Net, Res-U-Net) are particularly effective due to their ability to extract spatial features from high-resolution multispectral, hyperspectral, or RGB imagery. For instance, U-Net-based models have achieved over 90% overall accuracy in classifying coral reefs, macroalgae, and coastal wetlands. Attention-enhanced models further improve accuracies by dynamically focusing on discriminative regions. For ocean-based climate change solutions, CNN-based architectures (e.g., RFCN, ResNet) are frequently employed and have achieved strong performance in wind speed retrieval from image-based data sources such as SAR and GNSS-R. For sequential prediction tasks, such as ship fuel consumption forecasting, temporal models (e.g., CGRU, Bi-LSTM with attention, and TCN-GRU) have proven effective. For coastal floods, DL models such as CNN, U-Net, and hybrid models combining CNNs with LSTM have shown notable success in integrating multisource data (e.g., SAR, tide levels, and rainfall) to support flood mapping and forecasting. In fisheries management, object detection and segmentation models like Mask R-CNN, YOLOv4, and U-Net have demonstrated high accuracy in species classification and fishery monitoring tasks.  
This review also summarizes commonly used oceanographic datasets, such as satellite remote sensing, in situ observations, and reanalysis products. For each data type, representative sources and their typical applications in ocean research are presented. The review analyzes how different types of data affect the performance of DL models, highlighting their respective strengths and limitations. For example, satellite data offers broad spatial coverage but is prone to contain missing values; in situ observations are accurate but sparse, leading to overfitting; reanalysis data provide global coverage but may introduce systematic biases and over-smoothing. The review also discusses how the challenge of heterogeneous spatiotemporal resolutions in multisensory datasets is addressed through strategies such as interpolation, resampling, and deep-learning-based super-resolution techniques.