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

• URL: http://arxiv.org/abs/2409.15311v1
• Date: Thu, 5 Sep 2024 10:49:26 GMT
• Title: Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset
• Abstract summary: This paper presents the Landsat Irish Coastal (LICS) dataset, which aims to facilitate the development of deep learning methods for coastal water body segmentation. The dataset is used to evaluate various automated approaches for segmentation, with U-NET achieving the highest accuracy of 95.0%. The study suggests that deep learning approaches can be further improved with more accurate training data and by considering alternative measurements of erosion.
• Score: 2.3592914313389253
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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