Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research.

Glaciers are losing ice mass at unprecedented rates, increasing the need for accurate, year-round monitoring to understand frontal ablation, particularly the factors driving the calving process. Deep learning models can extract calving front positions from Synthetic Aperture Radar imagery to track seasonal ice losses at the calving fronts of marine- and lake-terminating glaciers. The current state-of-the-art model relies on ImageNet-pretrained weights. However, they are suboptimal due to the domain shift between the natural images in ImageNet and the specialized characteristics of remote sensing imagery, in particular for Synthetic Aperture Radar imagery. To address this challenge, we propose two novel self-supervised multimodal pretraining techniques that leverage SSL4SAR, a new unlabeled dataset comprising 9,563 Sentinel-1 and 14 Sentinel-2 images of Arctic glaciers, with one optical image per glacier in the dataset. Additionally, we introduce a novel hybrid model architecture that combines a Swin Transformer encoder with a residual Convolutional Neural Network (CNN) decoder. When pretrained on SSL4SAR, this model achieves a mean distance error of 293m on the “CAlving Fronts and where to Find thEm” (CaFFe) benchmark dataset, outperforming the prior best model by 67 m. Evaluating an ensemble of the proposed model on a multi-annotator study of the benchmark dataset reveals a mean distance error of 75 m, approaching the human performance of 38 m. This advancement enables precise monitoring of seasonal changes in glacier calving fronts.

Position changes of glacier calving fronts are important indicators for evaluating the health of ice sheet outlet glaciers and changes in ice dynamics. However, manual delineation of calving fronts in remote sensing imagery is a time-consuming task, resulting in potential large costs. Deep learning-based methods have made remarkable progress in automatically segmenting and delineating glacier calving fronts from remote sensing imagery. The relatively few remote sensing images and the limited geometric changes for glacier observations both reduce the diversity of the data and exacerbate the difficulty of accurate segmentation. Here we describe a novel automatic method for detecting glacier calving fronts in synthetic aperture radar (SAR) images, termed HookFormer. Our approach processes high-resolution (target) and low-resolution (context) inputs with a unified Transformer architecture. The global-local tokens from the context and the target branches are integrated purely by the proposed cross-attention mechanism and cross-interaction module to complement and enhance each other. Moreover, we redesign the HookFormer architecture based on the CNN model AMD-HookNet aiming to improve computational efficiency while achieving significant performance gains with only half of the model parameters/FLOPs. We conduct an in-depth analysis and make extensive comparisons based on the challenging glacier segmentation benchmark dataset CaFFe. As the first pure Transformer approach, HookFormer sets a new state of the art with a mean distance error of 353 m to the ground truth, outperforming the baseline, Swin-Unet, and AMD-HookNet by 53 %, 39 %, and 19 %, respectively.

Big advancements in the field of Deep Learning allow the automated extraction of glacier calving fronts from satellite imagery. However, current efforts on Synthetic Aperture Radar (SAR) imagery still produce coarse and partly spurious predictions. Therefore, in this study, a two-dimensional fully-connected Conditional Random Field (CRF) is incorporated into the post-processing of a deep learning-based calving front delineation pipeline. The CRF takes as input the artificial neural network prediction and the original SAR image. Experiments are undertaken using the newly introduced benchmark dataset CaFFe and results are compared to the associated baseline. By introducing the CRF into the post-processing of the baseline’s pipeline, the mean distance error of the front prediction is improved by an average of 27 meters. The code is available at https://github.com/EntChanelt/GlacierCRF.

Frontal dynamics of marine-terminating glaciers play a crucial role in glacier projections. To reduce manual effort, deep learning methods can be employed to extract calving front positions from satellite imagery automatically. The newly introduced benchmark dataset "CaFFe" (Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from SAR imagery) [1] provides multi-mission Synthetic Aperture Radar (SAR) imagery along with manually annotated calving fronts. CaFFe establishes a standardized framework for comparing deep learning techniques in glacier calving front extraction. By utilizing CaFFe to benchmark forthcoming deep learning models, researchers can identify the most promising directions for future research. A leaderboard of models can be accessed at https://paperswithcode.com/sota/calving-front-delineation-in-synthetic.

Knowledge on changes in glacier calving front positions is important for assessing the status of glaciers. Remote sensing imagery provides the ideal database for monitoring calving front positions; however, it is not feasible to perform this task manually for all calving glaciers globally due to time constraints. Deep-learning-based methods have shown great potential for glacier calving front delineation from optical and radar satellite imagery. The calving front is represented as a single thin line between the ocean and the glacier, which makes the task vulnerable to inaccurate predictions. The limited availability of annotated glacier imagery leads to a lack of data diversity (not all possible combinations of different weather conditions, terminus shapes, sensors, etc. are present in the data), which exacerbates the difficulty of accurate segmentation. In this article, we propose attention-multihooking-deep-supervision HookNet (AMD-HookNet), a novel glacier calving front segmentation framework for synthetic aperture radar (SAR) images. The proposed method aims to enhance the feature representation capability through multiple information interactions between low-resolution and high-resolution inputs based on a two-branch U-Net. The attention mechanism, integrated into the two branch U-Net, aims to interact between the corresponding coarse and fine-grained feature maps. This allows the network to automatically adjust feature relationships, resulting in accurate pixel classification predictions. Extensive experiments and comparisons on the challenging glacier segmentation benchmark dataset CaFFe show that our AMD-HookNet achieves a mean distance error (MDE) of 438 m to the ground truth outperforming the current state of the art by 42%, which validates its effectiveness.