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

2026-01-29

AI Summary

This study addresses the limited generalization of existing glacier calving front segmentation models to new observation sites due to domain shift. To overcome this challenge, the authors propose a novel few-shot domain adaptation method that requires no modification to the model architecture. For the first time, the approach integrates summer time-series reference imagery with spatially static geophysical priors to enable effective cross-domain knowledge transfer. The method substantially improves segmentation accuracy at unseen sites, reducing the average localization error from 1,131.6 meters to 68.7 meters. This advancement provides an efficient and scalable technical pathway for monitoring glacier dynamics at a global scale.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Few-Shot Domain Adaptation
• Glacier Calving Front Delineation
• Out-of-Distribution Domain
• Domain Shift
• Deep Learning Segmentation

Innovation

• Few-Shot Domain Adaptation
• Temporal References
• Static Priors
• Glacier Calving Front Delineation
• Out-of-Distribution Generalization