Draft:Shoreline Segmentation
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Shoreline segmentation is a specialized task within the broader field of water body segmentation in remote sensing and computer vision. While water body segmentation refers to the process of delineating all water-covered areas (such as lakes, rivers, and seas) within an image,[1] shoreline segmentation specifically targets the precise extraction of the boundary line between land and water—known as the shoreline.[2]
Importance and Challenges
[edit]Shoreline, as the land-water interface, is the home (within 100 km) to more than 2 billion people globally.[3] Accurately segmenting shorelines is essential for numerous environmental, engineering, and geospatial applications, including coastal monitoring, change detection, habitat assessment, and disaster response. Unlike general water body segmentation, shoreline segmentation places a high priority on the accurate localization of edges, particularly those that do not coincide with the image boundary. These internal shoreline edges are crucial for quantifying shoreline change, calculating erosion rates, and supporting hydrodynamic modeling.
Limitations of Conventional Metrics
[edit]Traditional evaluation metrics for image segmentation, such as Intersection over Union (IoU) and pixel accuracy, are designed to assess the overall overlap between predicted and ground truth regions.[4] However, these metrics do not fully capture the accuracy of the shoreline itself, especially the quality of the detected edge within the interior of the image. This is because a model may achieve high IoU or pixel accuracy by correctly labeling large water and land regions while still producing significant errors along the critical shoreline boundary.
To address this limitation, additional edge-focused evaluation measures—such as boundary IoU, Hausdorff distance, or contour-based accuracy—are increasingly used to assess shoreline segmentation performance. These specialized metrics are better suited for evaluating how closely the predicted shoreline matches the actual shoreline, which is often the most important aspect for practical coastal and environmental analysis.
References
[edit]- ^ Miao, Ziming; Fu, Kun; Sun, Hao; Sun, Xian; Yan, Menglong (April 2018). "Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks". IEEE Geoscience and Remote Sensing Letters. 15 (4): 602–606. Bibcode:2018IGRSL..15..602M. doi:10.1109/LGRS.2018.2794545. ISSN 1545-598X.
- ^ Toure, Seynabou; Diop, Oumar; Kpalma, Kidiyo; Maiga, Amadou Seidou (2019-02-05). "Shoreline Detection using Optical Remote Sensing: A Review". ISPRS International Journal of Geo-Information. 8 (2): 75. Bibcode:2019IJGI....8...75T. doi:10.3390/ijgi8020075. ISSN 2220-9964.
- ^ Cosby, A. G.; Lebakula, V.; Smith, C. N.; Wanik, D. W.; Bergene, K.; Rose, A. N.; Swanson, D.; Bloom, D. E. (2024-09-28). "Accelerating growth of human coastal populations at the global and continent levels: 2000–2018". Scientific Reports. 14 (1): 22489. Bibcode:2024NatSR..1422489C. doi:10.1038/s41598-024-73287-x. ISSN 2045-2322. PMC 11438952. PMID 39341937.
- ^ Rahman, Md Atiqur; Wang, Yang (2016). "Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation". In Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Porikli, Fatih; Skaff, Sandra; Entezari, Alireza; Min, Jianyuan; Iwai, Daisuke (eds.). Advances in Visual Computing. Lecture Notes in Computer Science. Vol. 10072. Cham: Springer International Publishing. pp. 234–244. doi:10.1007/978-3-319-50835-1_22. ISBN 978-3-319-50835-1.