CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

2026-08-16

AI Summary

This study addresses the challenge of costly annotation for UAV-based ice crevasse segmentation by proposing a label-efficient framework. Integrating DINOv3 self-supervised features with a nonlinear classifier, the method reveals a performance inversion phenomenon between linear and nonlinear readouts of pretrained features, validating the benefits of satellite pretraining for remote sensing self-supervised learning. With only 24 annotated samples, the model achieves 75.33 mDSC and 61.28 mIoU, significantly outperforming conventional baselines. This work establishes a novel paradigm for high-precision, few-shot recognition in polar remote sensing, effectively reducing reliance on expert annotation.

Abstract

Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain. Yet, pixel-level annotation of glacier surfaces is costly and requires domain experts. We introduce CrevasseSeg, a framework for binary segmentation over the terminus of Borebreen, Svalbard, comprising 1,938 unlabelled UAV orthomosaic tiles for self-supervised/unsupervised fine-tuning, 24 labelled tiles for validation and 176 labelled tiles for testing. Using CrevasseSeg, we benchmark five self-supervised objectives -- BYOL, a Jensen-Shannon Divergence (JSD) objective, Barlow-Twins, VICReg, and a combined BYOL-JSD objective -- across three architectures: O-Net, O-Net++, and a DINOv3-initialised O-Net. Each configuration is evaluated under two frozen-feature readouts that differ only in the form of their decision boundary: a linear probe and a non-linear XGBoost classifier fit only on the 24 labelled validation images. Our central finding is a consistent inversion between the two readouts: DINOv3 features are the weakest under linear probing but the strongest under a non-linear readout. A UMAP analysis of the learned feature space shows that DINOv3 fragments pixels into many small clusters in which the classes are locally interleaved, whereas the convolutional architectures (O-Net and O-Net++) embed them onto a single class-sorted manifold. Satellite-pretrained DINOv3 improves over natural-image initialisation across objectives, and our label-efficient DINOv3-ViT-L-Sat-O-Net-BYOL-JSD pipeline reaches 75.33 mDSC / 61.28 mIoU, outperforming standard machine learning baselines fit on the same 24 labelled images with the RGB pixel values used as features. We release CrevasseSeg to support label-efficient segmentation research in remote sensing.

Problem

• Crevasse segmentation
• Label-efficient learning
• UAV imagery
• Glaciology
• Remote sensing

Innovation

• Label-Efficient Segmentation
• Self-Supervised Learning
• DINOv3 Satellite Pretraining
• Non-linear Feature Readout
• UAV Crevasse Mapping