cs.LGAug 16, 2026

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

Authors: Steven Wallace, William D. Harcourt, Richard Hann, Aiden Durrant, Somayajulu Sripada, Georgios Leontidis

Organizations: School of Natural and Computing Sciences, University of Aberdeen, UK · Interdisciplinary Institute, University of Aberdeen, UK · School of Geosciences, University of Aberdeen, UK · Department of Engineering Cybernetics, Norwegian University of Science and Technology, Norway · 

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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.

Figures & tables

Split#TilesLabelledRole
Train1,938NoSSL fine-tuning
Val24YesFit readout / tune
Test176YesEvaluation
Table 1: CrevasseSeg split. Labels are available only for the validation and test sets.
Figure 1: Overview of the DINOv3-O-Net architecture. The frozen DINOv3 backbone extracts multi-scale features, which are passed through per-block adaptor layers (DINOv3 Adaptor) and a Feature Alignment/Pyramid Module (FAPM) before being progressively fused by a stack of decoders to produce the final binary crevasse segmentation mask output from XGBoost.
MethodsmDSCmIoU
DINOv3-ViT-L-O-Net-BYOL-JSD50.5534.98
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD48.2733.87
O-Net-BYOL-JSD71.7957.07
O-Net-Plus-Plus-BYOL-JSD71.4757.04
DINOv3-ViT-L-O-Net-BYOL48.6034.12
DINOv3-ViT-L-Sat-O-Net-BYOL48.2733.87
Table 2: Linear-probe evaluation on CrevasseSeg test (foreground mDSC, mIoU) for five SSL objectives ×\times three backbones. DINOv3 rows are weak and several collapse to the majority-class score.
MethodsmDSCmIoU
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD75.3361.82
DINOv3-ViT-L-O-Net-BYOL-JSD74.2360.31
O-Net-BYOL-JSD73.1559.25
O-Net-Plus-Plus-BYOL-JSD73.3559.58
DINOv3-ViT-L-Sat-O-Net-BYOL75.6862.43
DINOv3-ViT-L-O-Net-BYOL74.2360.29
Table 3: XGBoost (non-linear) readout on CrevasseSeg test. The ranking inverts: satellite-pretrained DINOv3-O-Net becomes strongest. Best per objective in bold .
MethodmDSCmIoU
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD (ours)75.3361.82
KNN70.9656.54
Random Forest71.5857.50
XGBoost71.4357.12
FNN71.8357.55
LSTM71.2556.95
Table 4: CrevasseSeg test: our label-efficient pipeline vs. standard baselines fit on the same 24 labels. Best in bold .
MethodmDSCmIoU
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD-Ensemble (ours)77.3064.54
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD (ours)75.3361.82
PriMaPs-Em-DINO-ViT-B75.0061.11
EAGLE-DINO-ViT-S77.5464.65
HP-ViT-S74.8560.95
STEGO-DINO-ViT-B76.4363.00
Table 5: CrevasseSeg test: our label-efficient pipeline vs. standard deep learning baselines non-linear evaluation. Best in bold .
MethodmDSCmIoU
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD-Ensemble (ours)73.6459.88
DINOv3-ViT-L-Sat-O-Net-BYOL-JSD (ours)48.2733.87
PriMaPs-Em-DINO-ViT-B74.8860.96
EAGLE-DINO-ViT-S75.5462.05
HP-DINO-ViT-S74.8560.95
STEGO-DINO-ViT-B75.8462.41
Table 6: CrevasseSeg test: our label-efficient pipeline vs. standard deep learning baselines linear evaluation. Best in bold .
Figure 2: Comparison between the input tile, ground truth mask, and masks from four evaluation methods, displaying the majority-class collapse under linear evaluation for DINOv3-ViT-L-Sat-O-Net-BYOL-JSD and not the other methods.
Decoder BlockmDSCmIoU
Block 10.70260.5586
Block 20.72910.5886
Block 30.48270.3387
Table 7: Layer-wise linear probing results for DINOv3-ViT-L-Sat-O-Net-BYOL-JSD decoder blocks, evaluated on upsampled features against pixel-level labels.
Figure 3: Two-dimensional UMAP projections of the frozen per-pixel features (BYOL–JSD objective) for the four backbones, coloured by class (class 0: ice/background, n=13,168n{=}13{,}168 ; class 1: crevasse/foreground, n=6,832n{=}6{,}832 ). The DINOv3 backbones (a, b) fragment pixels into many small clusters in which the two classes are locally interleaved, whereas the convolutional backbones (c, d) embed pixels onto a single connected manifold whose ends are dominated by different classes.
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