sidgan/ETCI-2021-Competition-on-Flood-Detection
Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training
This project helps disaster response teams and aid organizations quickly identify flooded areas after a natural disaster. By analyzing satellite imagery from Sentinel-1 SAR, it can pinpoint where flooding has occurred, providing critical information for early warning systems and efficient resource deployment. The output is a map highlighting flooded regions, enabling rapid assessment and response for those managing disaster relief efforts.
179 stars. No commits in the last 6 months.
Use this if you need to rapidly detect and map flood extents using satellite radar imagery for disaster management, humanitarian aid, or early warning systems.
Not ideal if you require flood prediction or hydrological modeling rather than post-event detection from satellite data.
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179
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Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Jun 19, 2022
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