Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery
Authors: Gourmelon Nora , Seehaus Thorsten , Braun Matthias , Maier Andreas , Christlein Vincent 
Published: May 2022

Abstract. Exact information on calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that could automatically detect the calving fronts on satellite imagery. Most studies use optical images, as in these images, calving fronts are often easy to distinguish due to sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on SAR images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions, e.g., cloud cover, facilitating all-year monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels to train and test approaches for the detection of glacier calving fronts. The dataset is the first to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background and one for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. Additionally, we introduce Atrous Spatial Pyramid Pooling to the bottleneck layer. In the following two post-processing procedures, the segmentation results are converted into one-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of the models, we first review the segmentation results using the recall, precision, F1-score, and the Jaccard Index. Second, we evaluate the front delineation by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, as compared to a laterally well constrained, single calving front of Mapple Glacier.