Jebeli, AtefehTama, Bayu AdhiJaneja, VandanaHolschuh, NicholasJensen, ClaireMorlighem, MathieuMacGregor, Joseph A.Fahnestock, Mark A.2024-08-272024-08-272023-07Jebeli, Atefeh, Bayu Adhi Tama, Vandana P. Janeja, Nicholas Holschuh, Claire Jensen, Mathieu Morlighem, Joseph A. MacGregor, and Mark A. Fahnestock. “TSSA: Two-Step Semi-Supervised Annotation for Radargrams on the Greenland Ice Sheet.” In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 56–59, 2023. https://doi.org/10.1109/IGARSS52108.2023.10282880.https://doi.org/10.1109/IGARSS52108.2023.10282880http://hdl.handle.net/11603/35807IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 16-21 July 2023, Pasadena, CA, USAIce-penetrating radar surveys have been conducted across the Greenland Ice Sheet since the 1960s, producing radargrams that measure ice thickness and detect the ice sheet’s radiostratigraphy. However, these radargrams are relatively under-explored and not yet fully annotated, mapped, or interpreted glaciologically. We aim to move towards automatic radargram annotation using deep learning-based methods. To provide a training set for these methods, we develop a two-step semi-supervised annotation (TSSA) approach that uses an existing unsupervised layer annotation (ARESELP) method and a deep learning-based segmentation approach (U-Net) to detect surface, and bottom reflectors (representing the bedrock) layers in radargrams. Here we focus on two evaluations of our approach: 1. Surface and bottom annotations; and 2. Data augmentation and transfer learning techniques for improving the performance of deep learning methods. Our study is a foundation for improving the efficacy of AI-based methods for auto-annotation of radargrams, where the training set is generated seamlessly through unsupervised learning.4 pagesen-US© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ice sheetTrainingComputational modelingsupervised learningDeep learningunsupervised learningAnnotationsData augmentationTransfer learningTransformersdeep neural networkice penetrating radarText