Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics

TL;DR

The paper addresses the need for accurate ice sheet annotation to support reliable sea level rise projections. It introduces a suite of quantitative metrics, including isochrone connectivity and various vision-based measures, and evaluates ARESELP and MARESELP against expert labels on a dataset of 100 radargrams from CReSIS in North Greenland. Results show that ARESELP substantially improves layer continuity over manual labeling, while MARESELP achieves the highest continuity and closest alignment with expert annotations according to several metrics, with some caveats about hallucinated layers in automated outputs. The proposed metrics enable reproducible validation and can enhance ice sheet structure analyses, contributing to more accurate assessments of ice dynamics and their impact on sea level.

Abstract

The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data interpretation by introducing a suite of quantitative metrics designed to validate ice sheet annotation techniques. Focusing on both manual and automated methods, including ARESELP and its modified version, MARESELP, we assess their accuracy against expert annotations. Our methodology incorporates several computer vision metrics, traditionally underutilized in glaciological research, to evaluate the continuity and connectivity of ice layer annotations. The results demonstrate that while manual annotations provide invaluable expert insights, automated methods, particularly MARESELP, improve layer continuity and alignment with expert labels.