Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset

TL;DR

This paper introduces the LICS dataset to improve deep learning-based segmentation of Irish coastal waters, demonstrating high accuracy with U-NET and NDWI benchmarks, and providing resources for future research.

Contribution

The paper presents the LICS dataset specifically designed for Irish coastal water segmentation, enabling more accurate deep learning models tailored to local conditions.

Findings

U-NET achieved 95.0% accuracy in segmentation.

NDWI benchmark outperformed U-NET with 97.2% accuracy.

Deep learning can be improved with better training data and erosion measures.

Abstract

Ireland's coastline, a critical and dynamic resource, is facing challenges such as erosion, sedimentation, and human activities. Monitoring these changes is a complex task we approach using a combination of satellite imagery and deep learning methods. However, limited research exists in this area, particularly for Ireland. This paper presents the Landsat Irish Coastal Segmentation (LICS) dataset, which aims to facilitate the development of deep learning methods for coastal water body segmentation while addressing modelling challenges specific to Irish meteorology and coastal types. The dataset is used to evaluate various automated approaches for segmentation, with U-NET achieving the highest accuracy of 95.0% among deep learning methods. Nevertheless, the Normalised Difference Water Index (NDWI) benchmark outperformed U-NET with an average accuracy of 97.2%. The study suggests that deep learning approaches can be further improved with more accurate training data and by considering alternative measurements of erosion. The LICS dataset and code are freely available to support reproducible research and further advancements in coastal monitoring efforts.