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

arXiv: https://arxiv.org/abs/2409.15311 · HTML: https://mlsanity.com/paper/2409.15311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation](https://mlsanity.com/paper/1709.00201/) (2017)
- [Link Climate: An Interoperable Knowledge Graph Platform for Climate Data](https://mlsanity.com/paper/2210.16050/) (2022)
- [Multi-label Cloud Segmentation Using a Deep Network](https://mlsanity.com/paper/1903.06562/) (2019)
- [A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2003.07784/) (2020)
- [High-Dynamic-Range Imaging for Cloud Segmentation](https://mlsanity.com/paper/1803.01071/) (2018)
- [Using GANs to Augment Data for Cloud Image Segmentation Task](https://mlsanity.com/paper/2106.03064/) (2021)
- [Rough Set Based Color Channel Selection](https://mlsanity.com/paper/1611.00931/) (2016)
- [Forecasting Precipitable Water Vapor Using LSTMs](https://mlsanity.com/paper/2006.15165/) (2020)
- [The Effectiveness of Edge Detection Evaluation Metrics for Automated Coastline Detection](https://mlsanity.com/paper/2405.11498/) (2024)
- [Machine Learning Techniques and Applications For Ground-based Image Analysis](https://mlsanity.com/paper/1606.02811/) (2016)
- [Automated Coastline Extraction Using Edge Detection Algorithms](https://mlsanity.com/paper/2405.11494/) (2024)
- [Interpreting a Semantic Segmentation Model for Coastline Detection](https://mlsanity.com/paper/2405.11500/) (2024)

## Built on by (most cited first, 1 of 1)

- [Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images](https://mlsanity.com/paper/2505.01884/) (2025)

## Abstract (arXiv)

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.