Summary of A Parallel Workflow For Polar Sea-ice Classification Using Auto-labeling Of Sentinel-2 Imagery, by Jurdana Masuma Iqrah et al.
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery
by Jurdana Masuma Iqrah, Wei Wang, Hongjie Xie, Sushil Prasad
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research develops a scalable system for classifying polar sea ice using Sentinel-2 (S2) images, crucial for monitoring global warming. The method utilizes carefully determined color thresholds to segment and automatically label S2 images, leveraging PySpark for parallel processing and achieving significant speedups. A U-Net machine learning model is trained on the auto-labeled data, resulting in good classification accuracy. The model is further distributed across 8 GPUs using Horovod framework, speeding up training without compromising accuracy. The trained model achieves a high classification accuracy of 98.97% for auto-labeled datasets when filtering out thin clouds and shadows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand the changing polar sea ice by creating a system to classify images from satellites like Sentinel-2. The system uses special colors and filters to separate different types of ice, which is important for tracking climate change. The method is fast and accurate because it can process many images at once using powerful computers. |
Keywords
* Artificial intelligence * Classification * Machine learning * Tracking
