Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866915761122967552 |
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| author | Dreier, Marcel Gourmelon, Nora Pyles, Dakota Seehaus, Thorsten Braun, Matthias H. Maier, Andreas Christlein, Vincent |
| author_facet | Dreier, Marcel Gourmelon, Nora Pyles, Dakota Seehaus, Thorsten Braun, Matthias H. Maier, Andreas Christlein, Vincent |
| contents | During benchmarking, the state-of-the-art model for glacier calving front delineation achieves near-human performance. However, when applied in a real-world setting at a novel study site, its delineation accuracy is insufficient for calving front products intended for further scientific analyses. This site represents an out-of-distribution domain for a model trained solely on the benchmark dataset. By employing a few-shot domain adaptation strategy, incorporating spatial static prior knowledge, and including summer reference images in the input time series, the delineation error is reduced from 1131.6 m to 68.7 m without any architectural modifications. These methodological advancements establish a framework for applying deep learning-based calving front segmentation to novel study sites, enabling calving front monitoring on a global scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21663 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation Dreier, Marcel Gourmelon, Nora Pyles, Dakota Seehaus, Thorsten Braun, Matthias H. Maier, Andreas Christlein, Vincent Computer Vision and Pattern Recognition During benchmarking, the state-of-the-art model for glacier calving front delineation achieves near-human performance. However, when applied in a real-world setting at a novel study site, its delineation accuracy is insufficient for calving front products intended for further scientific analyses. This site represents an out-of-distribution domain for a model trained solely on the benchmark dataset. By employing a few-shot domain adaptation strategy, incorporating spatial static prior knowledge, and including summer reference images in the input time series, the delineation error is reduced from 1131.6 m to 68.7 m without any architectural modifications. These methodological advancements establish a framework for applying deep learning-based calving front segmentation to novel study sites, enabling calving front monitoring on a global scale. |
| title | Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.21663 |