Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery

• Published in: Earth System Science Data
• Main Authors: Gourmelon, Nora, Seehaus, Thorsten, Braun, Matthias, Maier, Andreas, Christlein, Vincent
• Format: Text
• Language: English
• Published: 2022
• Subjects: Greenland; Antarc*; Antarctica; glacier; glaciers; polar night; Alaska
• Online Access: https://doi.org/10.5194/essd-14-4287-2022; https://essd.copernicus.org/articles/14/4287/2022/

Exact information on the 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 can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (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 year-round monitoring worldwide. In this paper, we present a benchmark dataset ( Gourmelon et al. , 2022 b ) of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front ( https://doi.org/10.1594/PANGAEA.940950 ). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset 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 label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented ...