Researchers have developed a new framework for high-resolution flood mapping using Sentinel-1 and Sentinel-2 satellite imagery. This approach addresses limitations such as cloud cover in optical data and speckle noise in radar data by introducing a new dataset for the contiguous United States and employing novel learning strategies. The framework utilizes a shift-invariant loss function to handle geolocation uncertainties and a Conditional Variational Autoencoder (CVAE) for generative despeckling, demonstrating significant improvements in flood mapping accuracy. AI
IMPACT This research could lead to more accurate and timely flood detection, improving disaster response and management.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for flood mapping.
- alphaXiv
- arXiv
- CatalyzeX
- conditional variational autoencoder
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
- Sentinel-1
- Sentinel-2
- U-Net
- UNet++: A Nested U-Net Architecture for Medical Image Segmentation
- United States
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