Benchmarking coastal boundary datasets in deep learning applications

Coastal areas are of high importance for both human development and Earth’s biosphere. However, coastal erosion threatens the balance between the geosphere, hydrosphere and biosphere, putting fragile ecosystems at risk. Human activities such as urbanization and climate change further exacerbate this natural phenomenon. Effective monitoring solutions are essential for mitigation strategies and for understanding land–ocean interactions. Given the vast size of coastal regions, automated tools for monitoring coastal boundaries are increasingly necessary. Artificial intelligence, particularly deep learning combined with remote sensing data, has shown promise in this domain. Currently, there is a lack of benchmarking studies for datasets relevant to this task. This study aims to fill that gap by comparing multiple remote sensing datasets for boundary extraction using deep learning. Benchmarking available datasets and models provides a foundation for standardization and future workflow integration. Seven datasets were compared and cross-tested using three popular deep learning algorithms. A novel metric based on pixel-level edge accuracy was developed and used to evaluate model performance. The results demonstrate the ability of deep learning algorithms to generalize efficiently across multiple datasets. The SNOWED dataset, in particular, achieved highly promising results with strong cross-dataset F1-scores, accurate boundary delineation and robust generalization. These findings highlight the potential for a resolution-agnostic and reliable framework for coastal boundary extraction using optical satellite imagery.