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Found 3 papers, 1 papers with code
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance
In this work, we explore a novel approach for supporting satellite-based flood index insurance by extracting high-resolution spatio-temporal information from news media.
Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping
The Sentinel-1 satellite is effective for flood detection, but for longer time series, other satellites such as MODIS can be used in combination with deep learning models to accurately identify and map past flood events.
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement
We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation.
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