Wikipedia

Shoreline segmentation

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

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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.[4][5][6] 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.[7] These internal shoreline edges are crucial for quantifying shoreline change, calculating erosion rates, and supporting hydrodynamic modeling.

Limitations of Conventional Metrics

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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.[8] 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.[9][10] 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, Frechet and Hausdorff distance, or contour-based accuracy—are increasingly used to assess shoreline segmentation performance. [11]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.

Dataset

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Several datasets have been developed for shoreline segmentation and related tasks. For shoreline segmentation specifically, the Coastal Aerial Imagery Dataset (CAID) was released in August 2025, comprising more than 20,000 manually annotated coastal aerial images dedicated to shoreline mapping.[7]

For multi-label datasets that include shoreline areas, examples include DeepGlobe,[12] ATLANTIS,[13] and GID. [14]These datasets contain hundreds to thousands of images featuring water–land interfaces; however, they were not explicitly designed for shoreline segmentation, as water is only one among many land cover classes represented.

Other datasets focus on water–land interaction zones, such as Sen1Floods11,[15] UrbanSARFloods,[16] DaliWS,[17] S1S2-Water,[18] WaterNet,[19] and SWED.[20] While these collections emphasize aquatic and flood environments, they are not tailored for shoreline segmentation and thus do not encompass the full diversity of shoreline landscapes.

Bibliography

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  • Ciecholewski, Marcin (March 2024). "Review of Segmentation Methods for Coastline Detection in SAR Images". Archives of Computational Methods in Engineering. 31 (2): 839–869. doi:10.1007/s11831-023-10000-7.
  • Erdem, Firat; Bayram, Bulent; Bakirman, Tolga; Bayrak, Onur Can; Akpinar, Burak (February 2021). "An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images". Advances in Space Research. 67 (3): 964–974. Bibcode:2021AdSpR..67..964E. doi:10.1016/j.asr.2020.10.043.
  • Jaszcz, Antoni; Włodarczyk-Sielicka, Marta; Stateczny, Andrzej; Połap, Dawid; Garczyńska, Ilona (27 November 2024). "Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements". Remote Sensing. 16 (23): 4457. Bibcode:2024RemS...16.4457J. doi:10.3390/rs16234457.

References

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  1. ^ 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.
  2. ^ 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.
  3. ^ 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.
  4. ^ Zoysa, Sanjana; Basnayake, Vindhya; Samarasinghe, Jayanga T.; Gunathilake, Miyuru B.; Kantamaneni, Komali; Muttil, Nitin; Pawar, Uttam; Rathnayake, Upaka (2023-05-06). "Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques". Sustainability. 15 (9): 7651. Bibcode:2023Sust...15.7651Z. doi:10.3390/su15097651. ISSN 2071-1050.
  5. ^ 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.
  6. ^ Cenci, Luca; Disperati, Leonardo; Persichillo, Maria Giuseppina; Oliveira, Eduardo R; Alves, Fátima L; Phillips, Michael (2018-05-04). "Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management". GIScience & Remote Sensing. 55 (3): 355–375. Bibcode:2018GISRS..55..355C. doi:10.1080/15481603.2017.1376370. ISSN 1548-1603.
  7. ^ a b Wang, Wei; Lu, Boyuan; Li, Yihan; Shi, Weiyan (2025). "Descriptor: Coastal Aerial Imagery Dataset for Shoreline Segmentation (CAID)". IEEE Data Descriptions: 1–11. doi:10.1109/IEEEDATA.2025.3599116. ISSN 2995-4274.
  8. ^ 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.
  9. ^ Cheng, Bowen; Girshick, Ross; Dollar, Piotr; Berg, Alexander C.; Kirillov, Alexander (2021). "Boundary IoU: Improving Object-Centric Image Segmentation Evaluation": 15334–15342. {{cite journal}}: Cite journal requires |journal= (help)
  10. ^ Aktaş, Ümit Ruşen; Can, Gülcan; Vural, Fatoş T. Yarman (November 2012). "Edge-aware segmentation in satellite imagery: A case study of shoreline detection". 7th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS): 1–4. doi:10.1109/PPRS.2012.6398319. hdl:11511/69553. ISBN 978-1-4673-4962-8.
  11. ^ Mascret, Ariane; Devogele, Thomas; Le Berre, Iwan; Hénaff, Alain (2006), Riedl, Andreas; Kainz, Wolfgang; Elmes, Gregory A. (eds.), "Coastline Matching Process Based on the Discrete Fréchet Distance", Progress in Spatial Data Handling: 12th International Symposium on Spatial Data Handling, Berlin, Heidelberg: Springer, pp. 383–400, doi:10.1007/3-540-35589-8_25, ISBN 978-3-540-35589-2, retrieved 2025-08-23
  12. ^ Demir, Ilke; Koperski, Krzysztof; Lindenbaum, David; Pang, Guan; Huang, Jing; Basu, Saikat; Hughes, Forest; Tuia, Devis; Raskar, Ramesh (2018-05-17), "DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images", 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 172–17209, arXiv:1805.06561, doi:10.1109/CVPRW.2018.00031, ISBN 978-1-5386-6100-0, retrieved 2025-08-23
  13. ^ Erfani, Seyed Mohammad Hassan; Wu, Zhenyao; Wu, Xinyi; Wang, Song; Goharian, Erfan (2022-03-01). "ATLANTIS: A benchmark for semantic segmentation of waterbody images". Environmental Modelling & Software. 149 105333. arXiv:2111.11567. Bibcode:2022EnvMS.14905333E. doi:10.1016/j.envsoft.2022.105333. ISSN 1364-8152.
  14. ^ Tong, Xin-Yi; Xia, Gui-Song; Lu, Qikai; Shen, Huanfeng; Li, Shengyang; You, Shucheng; Zhang, Liangpei (2020-02-01). "Land-cover classification with high-resolution remote sensing images using transferable deep models". Remote Sensing of Environment. 237 111322. arXiv:1807.05713. Bibcode:2020RSEnv.23711322T. doi:10.1016/j.rse.2019.111322. ISSN 0034-4257.
  15. ^ Bonafilia, Derrick; Tellman, Beth; Anderson, Tyler; Issenberg, Erica (2020). "Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1": 210–211. {{cite journal}}: Cite journal requires |journal= (help)
  16. ^ Zhao, Jie; Xiong, Zhitong; Zhu, Xiao Xiang (2024). "UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping": 419–429. {{cite journal}}: Cite journal requires |journal= (help)
  17. ^ Zhang, Shanshan; Li, Weibin; Wang, Rongfang; Liang, Chenbin; Feng, Xihui; Hu, Yanhua (2024-02-18). "DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images". Remote Sensing. 16 (4): 720. Bibcode:2024RemS...16..720Z. doi:10.3390/rs16040720. ISSN 2072-4292.
  18. ^ Wieland, Marc; Fichtner, Florian; Martinis, Sandro; Groth, Sandro; Krullikowski, Christian; Plank, Simon; Motagh, Mahdi (2024). "S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17: 1084–1099. Bibcode:2024IJSTA..17.1084W. doi:10.1109/JSTARS.2023.3333969. ISSN 2151-1535.
  19. ^ Erdem, Firat; Bayram, Bulent; Bakirman, Tolga; Bayrak, Onur Can; Akpinar, Burak (2021-02-01). "An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images". Advances in Space Research. 67 (3): 964–974. Bibcode:2021AdSpR..67..964E. doi:10.1016/j.asr.2020.10.043. ISSN 0273-1177.
  20. ^ Seale, Catherine; Redfern, Thomas; Chatfield, Paul; Luo, Chunbo; Dempsey, Kari (2022-09-01). "Coastline detection in satellite imagery: A deep learning approach on new benchmark data". Remote Sensing of Environment. 278 113044. Bibcode:2022RSEnv.27813044S. doi:10.1016/j.rse.2022.113044. ISSN 0034-4257.
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