Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring

Erosion is a critical geological process that degrades soil and poses significant risks to human settlements and natural habitats. As climate change intensifies, effective coastal erosion management and prevention have become essential for our society and the health of our planet. Given the vast extent of coastal areas, erosion management efforts must prioritize the most vulnerable and critical regions. Identifying and prioritizing these areas is a complex task that requires the accurate monitoring and forecasting of erosion and its potential impacts. Various tools and techniques have been proposed to assess the risks, impacts and rates of coastal erosion. Specialized methods, such as the Coastal Vulnerability Index, have been specifically designed to evaluate the susceptibility of coastal areas to erosion. Coastal boundaries, a critical factor in coastal erosion monitoring, are typically extracted from remote sensing images. Due to the extensive scale of coastal areas and the complexity of the data, manually extracting coastal boundaries is challenging. Recently, artificial intelligence, particularly deep learning, has emerged as a promising and essential tool for this task. This review provides an in-depth analysis of remote sensing and deep learning for extracting coastal boundaries to assist in erosion monitoring. Various remote sensing imaging modalities (optical, thermal, radar), platforms (satellites, drones) and datasets are first presented to provide the context for this field. Artificial intelligence and its associated metrics are then discussed, followed by an exploration of deep learning algorithms for extracting coastal boundaries. The presented algorithms range from basic convolutional networks to encoder–decoder architectures and attention mechanisms. An overview of how these extracted boundaries and other deep learning algorithms can be utilized for monitoring coastal erosion is also provided. Finally, the current gaps, limitations and potential future directions in this field are identified. This review aims to offer critical insights into the future of erosion monitoring and management through deep learning-based boundary extraction.