Summary of Wv-net: a Foundation Model For Sar Wv-mode Satellite Imagery Trained Using Contrastive Self-supervised Learning on 10 Million Images, by Yannik Glaser et al.
WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images
by Yannik Glaser, Justin E. Stopa, Linnea M. Wolniewicz, Ralph Foster, Doug Vandemark, Alexis Mouche, Bertrand Chapron, Peter Sadowski
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 European Space Agency’s Copernicus Sentinel-1 mission is a constellation of C-band synthetic aperture radar (SAR) satellites that provide unprecedented monitoring of the world’s oceans. The study uses nearly 10 million WV-mode images and contrastive self-supervised learning to train a semantic embedding model called WV-Net, which outperforms a comparable model pre-trained on natural images with supervised learning in multiple downstream tasks. These tasks include estimating wave height, near-surface air temperature, and performing multilabel-classification of geophysical and atmospheric phenomena. The study demonstrates that WV-Net embeddings can support geophysical research by providing a convenient foundation model for various data analysis and exploration tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The European Space Agency’s Copernicus Sentinel-1 mission is a group of satellites that take pictures of the world’s oceans from space. They are very good at doing this, even when there are clouds in the way or it’s daytime. The problem is that people need to look at each picture and label what they see before they can use computers to learn more about the ocean. This takes a lot of time and makes it hard for scientists to use these pictures. A new computer program called WV-Net was made by looking at almost 10 million of these satellite images without needing labels. It’s very good at doing things like measuring wave height, temperature near the surface, and recognizing different weather phenomena. This means that scientists can now more easily study the ocean using these satellites. |
Keywords
» Artificial intelligence » Classification » Embedding » Self supervised » Supervised » Temperature
