AI-ready Snow Radar Echogram Dataset (SRED) for climate change monitoring

TL;DR

This paper introduces the first comprehensive, annotated radar echogram dataset for deep learning, enabling improved ice sheet layer tracking and climate change monitoring through benchmarking of various models.

Contribution

It provides a large, well-annotated radar echogram dataset derived from NASA's Operation Ice Bridge, facilitating standardized testing and development of deep learning algorithms for ice sheet analysis.

Findings

Deep learning models can identify snow layers in echograms

Advanced models can directly estimate snow depth from echograms

The dataset enables benchmarking and comparison of algorithms

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.