Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation
This enables glacier calving front monitoring on a global scale by adapting models to new sites, though it is incremental as it builds on existing methods without architectural changes.
The paper tackled the problem of insufficient delineation accuracy when applying a state-of-the-art glacier calving front model to novel out-of-distribution sites, reducing the delineation error from 1131.6 m to 68.7 m using a few-shot domain adaptation strategy with spatial static priors and summer reference images.
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.