{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hed-unet-combined-segmentation-and-edge","title":"HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline","arxiv_id":"2103.01849","date":"2021-03-02","proceeding":null,"authors":["Konrad Heidler","Lichao Mou","Celia Baumhoer","Andreas Dietz","Xiao Xiang Zhu"],"abstract":"Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \\url{https://github.com/khdlr/HED-UNet}.","url_abs":"https://arxiv.org/abs/2103.01849v1","url_pdf":"https://arxiv.org/pdf/2103.01849v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hed-unet-combined-segmentation-and-edge","repo_url":"https://github.com/khdlr/HED-UNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"hed-unet-combined-segmentation-and-edge","repo_url":"https://github.com/2023-MindSpore-1/ms-code-214/tree/main/hed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hed-unet-combined-segmentation-and-edge","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/hed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hed-unet-combined-segmentation-and-edge","repo_url":"https://github.com/MindSpore-paper-code-3/code4/tree/main/hed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hed-unet-combined-segmentation-and-edge","repo_url":"https://github.com/code-implementation1/Code4/tree/main/hed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"edge-detection","task_name":"Edge Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}