BDCN_UNet: Advanced shoreline extraction techniques integrating deep learning

Abstract The delineation of shorelines, marking the dynamic boundary between land and sea, is crucial for coastal management and ecological conservation. This research investigates various methods for automatic shoreline extraction from satellite images, utilizing cost-effective and regularly available remote sensing data. A deep learning framework is used for the comparison that combines semantic segmentation (UNEt) and edge detection (Bi-Directional Cascade Network, or BDCN) to make the results more accurate. The study focuses on the northern coast of Egypt, a region with diverse landscapes and significant human interventions. We’ve made progress in our methods by using advanced image processing, the CoastSat Toolkit, the BDCN_UNet framework, time series analysis, and deep learning networks to look at and keep an eye on Egypt’s North Coast’s future plans in more detail. This study captures the intricate interplay between natural and human factors in coastal environments, offering valuable insights for sustainable coastal development. The performance analysis of four models (UNet, Pyramid Scene Parsing Network, DeepLabV3, and BDCN_UNet) applied to three satellite datasets (Sentinel-2, PlanetScope, and Pleiades) showed that PlanetScope and Pleiades consistently yield higher accuracy (0.90–0.99), while Sentinel-2 proved more challenging, particularly for Pyramid Scene Parsing Network and DeepLabV3. UNet and BDCN_UNet exhibited robust performance across all datasets, especially with Pleiades delivering the best results overall.