## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Applications of Machine Learning and Artificial Intelligence in Coastal Monitoring
These impressive results were achieved using the WaterNet model, an ensemble deep learning approach designed for automatic shoreline segmentation from Landsat 8 OLI images specifically from Landsat 8 OLI data. WaterNet integrates five different architectures: Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2 Pix. Each method has its own unique strengths and is applied differently: U-Net is effective for biomedical image segmentation, Dilated U-Net eliminates pooling layers to preserve resolution, Fractal U-Net enhances accuracy by combining different visual feature levels, FC-DenseNet takes dense blocks in advance to increase feature sharing, and Pix2 Pix utilizes generative adversarial networks (GANs) for pixel-to-pixel image translation. WaterNet employs a majority voting algorithm, where each model \"votes\" and decides on the classification of each pixel, enhancing robustness, accuracy, and reliability. The ensemble approach results in high precision, with Intersection over Union (IoU) and F1 scores surpassing 99%, making it highly reliable for real-time coastal monitoring and resource management. [PERSON] et al. [21] provided an overview of shoreline extraction techniques, particularly in Multispectral Imaging (MSI) and ML applications. The article's discussion ranged from the more traditional approach, water indexing, to an advanced ML and image segmentation technique. One of the key points this work showed is the difficulty of elaborating a method that can be universally applied to monitoring shorelines regardless of type and location on Earth. This is because of the variation of coastlines, such as sandy beaches, cliffs, and dunes, for which individual extraction needs are expected. In this paper, applying Convolutional Neural Network (CNN)-based machine learning methods will improve shoreline detection accuracy with large-scale monitoring. On the other hand, this review focuses on certain points of attention in current approaches concerning the need for automation and scaling up of methods, with particular attention to shoreline extraction at a global scale. Conclusively, the overall suggestion of this paper is that integrating MSI with machine learning can ensure more sustainable and efficient shore monitoring. However, the further refinement of methods will be necessary for broader application. This is in agreement with, for instance, [PERSON] et al. [20], whereby these ensemble learning techniques, including WaterNet, were applied to automate shoreline extraction from satellite images by merging multiple deep learning models to improve the models' precision and reliability. In comparison to other reviews on coastal monitoring technologies, this paper aims to emphasize the incorporation of advanced methodologies, particularly AI and UAVs. However, integrating insights from studies like [PERSON] et al., 2022, could enhance the discussion by offering a global perspective on shoreline detection through the use of multispectralimaging and AI. [PERSON] et al., 2022, offered a comprehensive analysis of Convolutional Neural Networks (CNNs) for shoreline mapping, emphasizing their adaptability across diverse geographic and environmental contexts. For instance, their work detailed how traditional CNN-based models perform well in processing multispectral datasets from Sentinel-2 and Landsat but may struggle in environments with complex geomorphology, such as sediment-heavy coasts or vegetated shorelines [21]. Referencing [PERSON] et al. allows a direct benchmark comparison for WaterNet. While WaterNet emphasizes high classification accuracy and reduced error margins, [PERSON] et al., 2022, provided insights into the computational efficiency and scalability of CNN-based models for large-scale shoreline monitoring. A comparison of this nature would enhance the discourse by placing WaterNet within the larger framework of AI-driven coastal monitoring technologies, thereby highlighting its advantages in relation to conventional methodologies [21]. Furthermore, [PERSON] et al.