Leveraging Self-Supervised Learning for Sea Ice Segmentation in the Arctic to Reduce on Labelling

The rapid decline of Arctic sea ice driven by climate change poses significant challenges and opportunities for global shipping, ecosystems, and coastal communities. Understanding and mapping sea ice variability is crucial for assessing its implications on navigability and ensuring maritime safety in this dynamic region. One of the most significant challenges in applying machine learning (ML) to cryospheric sciences is the reliance on large quantities of human-labelled data, which is both costly and time-intensive to produce, particularly in remote and harsh environments like the Arctic. This contribution addresses this challenge by leveraging self-supervised learning (SSL) techniques and Convolutional Neural Network (CNN) to reduce the dependency on labelled data while maintaining high levels of model performance. We used the well-known UNet model, a CNN designed for pixel-wise segmentation tasks, and integrate BYOL (Bootstrap Your Own Latent), an SSL technique that leverages unlabelled data to learn features without requiring explicit labels. BYOL trains the model to match representations of the same image under different transformations, allowing it to learn useful features from unlabelled data without needing explicit labels.

We apply these models to Sentinel-1 SAR imagery in the Canadian Arctic Archipelago, a region of critical importance due to its role in global shipping routes, where sea ice variability directly impacts navigability and maritime safety.

We created binary ice and open water labels to serve as a benchmark for evaluating model performance. Early preliminary results suggest that using BYOL reduces the labelling requirement by approximately 50% compared to models trained without self-supervised pretraining. By pretraining the UNet model on unlabelled Sentinel-1 SAR imagery and fine-tuning it for sea ice segmentation, this approach demonstrates how leveraging unlabelled data can significantly minimise the need for human annotation while maintaining robust segmentation accuracy. These methods optimise the use of limited labelled datasets, enabling efficient and scalable models that potentially generalise to sea ice segmentation tasks where high-quality labels are often scarce or imprecise. These techniques enhance the adaptability of ML models, allowing them to be applied to new datasets and tasks with minimal retraining, further reducing the computational and data requirements. By reducing reliance on labelled data, this approach improves efficiency and opens up possibilities for tackling broader challenges, such as real-time ice monitoring, assessing shipping route viability, and conducting long-term trend analysis.