AI-ready Snow Radar Echogram Dataset (SRED) for climate change monitoring
Journal:
arXiv
Published Date:
May 1, 2025
Abstract
Tracking internal layers in radar echograms with high accuracy is essential
for understanding ice sheet dynamics and quantifying the impact of accelerated
ice discharge in Greenland and other polar regions due to contemporary global
climate warming. Deep learning algorithms have become the leading approach for
automating this task, but the absence of a standardized and well-annotated
echogram dataset has hindered the ability to test and compare algorithms
reliably, limiting the advancement of state-of-the-art methods for the radar
echogram layer tracking problem. This study introduces the first comprehensive
``deep learning ready'' radar echogram dataset derived from Snow Radar airborne
data collected during the National Aeronautics and Space Administration
Operation Ice Bridge (OIB) mission in 2012. The dataset contains 13,717 labeled
and 57,815 weakly-labeled echograms covering diverse snow zones (dry, ablation,
wet) with varying along-track resolutions. To demonstrate its utility, we
evaluated the performance of five deep learning models on the dataset. Our
results show that while current computer vision segmentation algorithms can
identify and track snow layer pixels in echogram images, advanced end-to-end
models are needed to directly extract snow depth and annual accumulation from
echograms, reducing or eliminating post-processing. The dataset and
accompanying benchmarking framework provide a valuable resource for advancing
radar echogram layer tracking and snow accumulation estimation, advancing our
understanding of polar ice sheets response to climate warming.
