Summary of A Physics-guided Neural Network For Flooding Area Detection Using Sar Imagery and Local River Gauge Observations, by Monika Gierszewska et al.
A physics-guided neural network for flooding area detection using SAR imagery and local river gauge observations
by Monika Gierszewska, Tomasz Berezowski
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel physics-guided neural network is proposed for detecting flooding extent areas in river valleys using Sentinel 1 time-series images and water elevations as input data. The model’s effectiveness is evaluated in five study areas by comparing predicted water maps with reference water maps obtained from digital terrain models and optical satellite images. Results show an IoU score of 0.89 for the water class and 0.96 for the non-water class, outperforming other unsupervised methods, especially during low water elevation conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to use satellite images to measure flooding in rivers. They used special computer models that learn from pictures taken by satellites to find where the water is and how big it is. The model works better than others when the river is not very full, which is important for people who need to know about floods. |
Keywords
» Artificial intelligence » Neural network » Time series » Unsupervised
