构建爱尔兰海岸水体分割数据集，助力遥感监测与模型优化
Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset
- 构建LICS数据集，聚焦爱尔兰气象与海岸类型特性
- U-Net达95.0%准确率，但NDWI基准方法更优（97.2%）
- 适合遥感、地理信息与环境监测研究者使用
爱尔兰海岸是关键且动态的资源，面临侵蚀、沉积和人类活动等挑战。监测这些变化复杂，本文结合卫星影像与深度学习方法应对。然而，针对爱尔兰的研究仍有限。本文提出Landsat Irish Coastal Segmentation (LICS) 数据集，旨在推动深度学习在海岸水体分割中的应用，并解决爱尔兰气候与海岸类型带来的建模难题。该数据集用于评估多种自动分割方法，其中U-Net在深度学习方法中表现最佳，准确率达95.0%；但基准方法NDWI平均准确率更高，达97.2%。研究建议通过更精准的训练数据和替代侵蚀度量方式进一步提升深度学习效果。LICS数据集与代码已公开，支持可复现研究及海岸监测进展。
原文摘要 · Abstract (English)
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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