DOI: 10.5281/zenodo.13995477
title: Ice Anatomy: A Benchmark Dataset and Methodology for Automatic Ice Boundary Extraction from Radio-Echo Sounding Data

description: The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of their bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data, to facilitate straightforward comparisons of deep learning models in the future. The ``IceAnatomy'' dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45,000km of observations. The RES data originates from three sources: FAU, CReSIS, and AWI. The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data was acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Icefield. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train-test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the ``omni'' model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve  Mean Meter Errors of 2.1m, 22.5m, and 4.9m for the ice surface and 9.1m, 71.6m, and 29.3m for the ice bottom. In relation to the mean measured ice thickness, these errors equate to 1.2%, 3.0%, and 0.3% for the ice surface and  4.9%, 9.6%, and 1.5% for the ice bottom.

 

 For more information, please read the following paper:

[Coming soon. Currently under review.]

Please also cite this paper if you plan on using the dataset.

 

For the implementation of a baseline model please visit:

[Coming soon]

 
description: This research was funded by the Bayerisches Staatsministerium für Wissenschaft und Kunst within the Elite Network Bavaria with the Int. Doct. Program ``Measuring and Modelling Mountain Glaciers in a Changing Climate'' (IDP M3OCCA)), as well as the German Research Foundation (DFG) project ``Large-scale Automatic Calving Front Segmentation and Frontal Ablation Analysis of Arctic Glaciers using Synthetic-Aperture Radar Image Sequences (LASSI)'' and the DFG project ``Ice thickness, remote sensing and sensitivity experiments using ice-flow modelling for major outlet glaciers of the Southern Patagonian Icefield''(ITERATE) grant DFG BR 2105/29-1/FU 1032/12-1.The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR projects b110dc and b194dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG – 440719683.We acknowledge the use of data and/or data products from CReSIS generated with support from the University of Kansas, NASA Operation IceBridge grant NNX16AH54G, NSF grants ACI-1443054, OPP-1739003, and IIS-1838230, Lilly Endowment Incorporated, and Indiana METACyt Initiative. Furthermore, we also thank the support of Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung. (2016). Polar aircraft Polar5 and Polar6 operated by the Alfred Wegener Institute. Journal of large-scale research facilities JLSRF, 2 (0), 87. doi: 10.17815/jlsrf-2-153. The authors would like to thank Aspen Technology, Inc. for providing licenses in the scope of the Aspen Technology, Inc. Academic Program.