Climate AI Nordics

Climate AI Nordics

Nordic network for collaborations around tackling climate change using AI and machine learning.

Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery

Event date: 2025-10-23

Event location:

Welcome to this week’s Learning Machines seminar.

This seminar is a collaboration between RISE and Climate AI Nordics – climateainordics.com.

Title: Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery

Speaker: Nora Gourmelon, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Abstract: The factors influencing melt at glacier fronts facing the ocean remain an active area of research. To better understand these factors, it is essential to monitor changes in glacier calving fronts. Due to the importance of weather-dependent and seasonal variations, Synthetic Aperture Radar (SAR) is the preferred imaging method for such monitoring. In recent years, deep learning models have been developed to automate the extraction of calving front positions, however, their performance on SAR data remains suboptimal.

Limited labeled data and high variability in SAR images hinder traditional supervised learning. Foundation models pre-trained on large, diverse datasets could provide robust feature representations that require minimal labeled data for fine-tuning on specific SAR tasks. However, in preliminary experiments, we found that the domain gap is too large for the task of extracting calving fronts from SAR imagery, and foundation models fine-tuned on the "CAlving Fronts and where to Find thEm" (CaFFe) benchmark dataset performed subpar.

Therefore, we compiled an unlabeled dataset of Sentinel-1 SAR image sequences of Arctic glaciers, each associated with a single Sentinel-2 optical reference image. Using this dataset, we developed a novel multi-modal self-supervised pre-training strategy and applied it to pre-train a hybrid CNN-transformer model. Fine-tuning on the CaFFe benchmark showed that the pre-trained model outperforms its non-pre-trained counterpart, enabling more robust calving front segmentation and demonstrating the potential of data-efficient learning for SAR imagery.

About the speaker: Nora Gourmelon is a PhD student at Friedrich Alexander Universität Erlangen Nürnberg, specializing in AI for Earth. Her current research focuses on automating glacier calving front segmentation in Synthetic Aperture Radar satellite imagery. She holds an M.Sc. in computer science with honors, complemented by study abroad semesters at NTNU (Norway) and SMU (Canada). She actively contributes to promoting wellbeing and equity within her lab, engaging in mental health and gender-equality initiatives. Recognized as an AI Newcomer by the German Informatics Society in 2023, her interdisciplinary research bridges computer vision, remote sensing, and cryospheric science.

Location: This is an online seminar. Connect using Zoom.

Date: 2025-10-23 15:00

Upcoming seminars:

  • 2025-11-06: Felix Koehler, TU Munich
  • 2025-11-20: Katarzyna Michalowska, SINTEF
  • All seminars are 15:00 CET.

More information and coming seminars: https://ri.se/lm-sem

– The Learning Machines Team

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