## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.1 DL Applications for Coral Reef Monitoring
\begin{table}
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**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline \hline  & [PERSON] et al. [197] & Classification & GF-2 MSI image & GCU-Net, a U-Net model integrating convolutional attention and geospatial cognition & North Reef: OA = 90.46\%, Kappa = 0.88. \\ \hline [PERSON] et al. [196] & Classification & Planet Dove Satellite RGB imagery; reef extent data (MCRMP) & Dense U-Net, recall = 0.59, F1-score = 0.66, Accuracy = 0.93. \\ \hline [PERSON] et al. [195] & Classification & Leica AD540 MSI; ICNet-2 LiDAR data; NOAA-provided bathymetry LiDAR data; Puerto Rico Brathic Habitats and Geomorphic Zone Classification Map & CNN and RF Kappa = 0.9013; Punta Kappa = 0.8735. \\ \hline [PERSON] et al. [198] & Classification & ICNet-2 LiDAR data; MSI provided by Sentinel-2 and PlanetScope; ground-truth benthic images from in situ sampling & CR Transformer & Accuracy = 95.71\%, mIoU = 91.25\%. \\ \hline Coral Reef & [PERSON] et al. [202] & Classification & RGB images (Moorea IDEA project) with manual annotations & Cnet & mIoU = 81.83\%, F1-score = 89.87\%. \\ \hline [PERSON] et al. [203] & 3D Mapping & Ego-motion videos & U-Net with ResNet34 backbone for depth and pose estimation; U-Net with ResNet35 backbone for segmentation & Total pixel accuracy = 84.1\%, Mean class accuracy = 68.8\%; 3D reconstruction in 18 frames per second. \\ \hline [PERSON] et al. [205] & Coral Bleaching Detection & RGB imagery collected by a drone over 5 time periods; Ground truth data collected via in situ transects during three time periods. & mRES-uNet classification & Unbleached coral classification & Precision = 0.96, Recall = 0.92; Bleached coral classification & Recall = 0.58. \\ \hline [PERSON] et al. [206] & Coral Bleaching Detection & Environmental variables including coral cover, depth, latitude, longitude, distance to shore, temperature, etc. (Global Coral Bleaching Database) & MLP & Coral bleaching was consistently linked to high sea-surface temperatures and temperature anomalies. \\ \hline [PERSON] et al. [211] & Seaweed Classification & Sentinel-2 MSI; spectral and coordinate measurements from field sampling & U-Net, DeepLabv3, SegNet & UNet achieved the highest accuracy for Lvshunkou Region: OA = 94.56\%, Kappa = 0.905, and for Jinzhou Region: OA = 94.68\%, Kappa = 0.913. \\ \hline [PERSON] et al. [212] & Seaweed Classification & Underwater stereo camera images & DeepLabV3+ & Seaweed segmentation IoU = 0.9. \\ \hline [PERSON] et al. [213] & Kelp Monitoring & MSI from Landsat-5 and 8 & Mask R-CNN & Dice Coefficient = 0.93 \(\pm\) 0.04. \\ \hline [PERSON] et al. [214] & Kelp Monitoring & Landsat satellite imagery; sUAS imagery (color, multispectral, hyperspectral); underwater imaging & CNN & Kelp detection accuracy achieved 91\%. \\ \hline [PERSON] et al. [215] & Macroalgae Classification & MSI from MicaSense RedEdge3 camera; In situ surveys data & U-Net with a VGG-13 encoder & F1-score = 87.79\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Illustrative examples of DL applications across different marine ecosystems.