Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics
It addresses the need for reliable ice sheet data to understand sea level rise, but is incremental as it builds on existing methods with new metrics.
This study tackled the problem of accurately interpreting ice sheet data by introducing quantitative metrics to validate annotation techniques, finding that automated methods like MARESELP improved layer continuity and alignment with expert labels.
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.