Evaluating the Impact of Super-Resolution for Coastal Boundary Segmentation Using Deep Learning for High-Resolution Imagery

Abstract. Coastal areas play an important role economically, socially and environmentally due to their many functions. However, these regions are at risk of erosion, which is further exacerbated by human-driven climate change. Tracking and monitoring coastal boundaries enable efficient allocation of conservation and protection efforts. Due to the vast size and complexity of coastal areas, on-site monitoring to track erosion is inefficient. Artificial intelligence has shown impressive results in segmenting and extracting these boundaries from remote sensing imagery. Historical remote sensing data make it possible to track long-term erosion but remain challenging due to the coarse resolution of older data. Our work proposes studying the impact of super-resolution on coastal boundary segmentation using high-resolution imagery. ESRGAN and SRCNN have proven highly beneficial in improving the quality of coarse-resolution samples, achieving superior performance compared to bicubic interpolation across scaling factors ranging from ×2 to ×12. ESRGAN super-resolved samples achieved F1-scores ranging from 97.75% to 89.92% for scaling factors ×2 to ×12, while bicubic interpolation achieved between 97.34% and 65.27%. These improvements demonstrate that SR enhances boundary delineation and robustness across scales. Our work also explores the applicability of tracking erosion through historical data. Results demonstrate a coastal boundary change of 0.23 m per year over seven years, which is on par with expected values.