Summary of Enhancing Coastal Water Body Segmentation with Landsat Irish Coastal Segmentation (lics) Dataset, by Conor O’sullivan et al.
Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset
by Conor O’Sullivan, Ambrish Kashyap, Seamus Coveney, Xavier Monteys, Soumyabrata Dev
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to monitoring Ireland’s dynamic coastline, a critical resource facing challenges like erosion and sedimentation, is presented in this paper. By combining satellite imagery with deep learning methods, researchers aim to facilitate the development of models for coastal water body segmentation while addressing unique challenges posed by Irish meteorology and coastal types. The Landsat Irish Coastal Segmentation (LICS) dataset is introduced, which serves as a benchmark for evaluating various automated approaches for segmentation. Deep learning models like U-NET achieve high accuracy, but are outperformed by the Normalised Difference Water Index (NDWI) benchmark. The study suggests that further improvements can be made with more accurate training data and alternative erosion measurements. This work has implications for coastal monitoring efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ireland’s coastline is important and changing due to erosion, sedimentation, and human activities. Scientists are using a combination of satellite images and special computer models to track these changes. They created a new dataset called LICS that helps them develop better models for identifying different parts of the coast. The dataset was used to test various automated methods, with one model called U-NET doing well. However, another method called NDWI did even better. The study suggests that making more accurate training data and using different ways to measure erosion could help improve these computer models. |
Keywords
» Artificial intelligence » Deep learning
