Yannik Glaser, Justin Stopa, Linnea Wolniewicz, Ralph Foster, Doug Vandemark, Alexis Mouche, Bertrand Chapron, Peter Sadowski
We lifted 2 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hawaii-ai/WVNet | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| init_dataset | Ran | hawaii-ai/WVNet/wvnet/data_utils.py pointer only (licence: NONE) · get_code("df69a1526332354e") |
| load_from_weights | Not yet run | hawaii-ai/WVNet/wvnet/model_utils.py pointer only (licence: NONE) · get_code("061403ab92883f71") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The European Space Agency's Copernicus Sentinel-1 (S-1) mission is a constellation of C-band synthetic aperture radar (SAR) satellites that provide unprecedented monitoring of the world's oceans. S-1's wave mode (WV) captures 20x20 km image patches at 5 m pixel resolution and is unaffected by cloud cover or time-of-day. The mission's open data policy has made SAR data easily accessible for a range of applications, but the need for manual image annotations is a bottleneck that hinders the use of machine learning methods. This study uses nearly 10 million WV-mode images and contrastive self-supervised learning to train a semantic embedding model called WV-Net. In multiple downstream tasks, WV-Net outperforms a comparable model that was pre-trained on natural images (ImageNet) with supervised learning. Experiments show improvements for estimating wave height (0.50 vs 0.60 RMSE using linear probing), estimating near-surface air temperature (0.90 vs 0.97 RMSE), and performing multilabel-classification of geophysical and atmospheric phenomena (0.96 vs 0.95 micro-averaged AUROC). WV-Net embeddings are also superior in an unsupervised image-retrieval task and scale better in data-sparse settings. Together, these results demonstrate that WV-Net embeddings can support geophysical research by providing a convenient foundation model for a variety of data analysis and exploration tasks. Preprint. Under review.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.18765")
get_code_for_paper("2406.18765")
have("2406.18765")
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