Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915940907614208 |
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| author | Gourmelon, Nora Heidler, Konrad Loebel, Erik Cheng, Daniel Klink, Julian Dong, Anda Wu, Fei Maul, Noah Koch, Moritz Dreier, Marcel Pyles, Dakota Seehaus, Thorsten Braun, Matthias Maier, Andreas Christlein, Vincent |
| author_facet | Gourmelon, Nora Heidler, Konrad Loebel, Erik Cheng, Daniel Klink, Julian Dong, Anda Wu, Fei Maul, Noah Koch, Moritz Dreier, Marcel Pyles, Dakota Seehaus, Thorsten Braun, Matthias Maier, Andreas Christlein, Vincent |
| contents | Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning Gourmelon, Nora Heidler, Konrad Loebel, Erik Cheng, Daniel Klink, Julian Dong, Anda Wu, Fei Maul, Noah Koch, Moritz Dreier, Marcel Pyles, Dakota Seehaus, Thorsten Braun, Matthias Maier, Andreas Christlein, Vincent Computer Vision and Pattern Recognition Machine Learning I.4.6; I.5.4 Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research. |
| title | Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.6; I.5.4 |
| url | https://arxiv.org/abs/2501.05281 |