A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery

no code implementations • 19 Mar 2024 • Jurdana Masuma Iqrah, Wei Wang, Hongjie Xie, Sushil Prasad

Using the Antarctic's Ross Sea region as an example, the U-Net model trained on auto-labeled data achieves a classification accuracy of 98. 97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model

no code implementations • 8 Mar 2023 • Jurdana Masuma Iqrah, Younghyun Koo, Wei Wang, Hongjie Xie, Sushil Prasad

Global warming is an urgent issue that is generating catastrophic environmental changes, such as the melting of sea ice and glaciers, particularly in the polar regions.