Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation

TL;DR

This paper presents a few-shot domain adaptation method that significantly improves glacier calving front delineation accuracy at new sites by leveraging static priors and summer references, enabling scalable global monitoring.

Contribution

It introduces a novel few-shot domain adaptation framework with static priors and seasonal references, enhancing glacier calving front delineation in out-of-distribution settings without changing model architecture.

Findings

Delineation error reduced from 1131.6 m to 68.7 m

Method enables accurate calving front monitoring at new sites

Framework applicable for global glacier monitoring

Abstract

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.