Deep Learning Approach for Flood Mapping Using Satellite Images Dataset

The use of advanced deep learning algorithms in recent research has improved the accuracy of flood mapping, which is critical for disaster management. With the help of attention mechanisms added to the U-Net architecture, this study suggests a flood mapping method. Attention methods help to improve focus on relevant features, and U-Net performs exceptionally well in semantic segmentation tasks, effectively capturing contextual information. The model distinguishes well between flooded and non-flooded areas, showing comparable performance than previous studies. By allowing the network to focus on specific crucial areas, the attention mechanisms improve the accuracy of defining the flood extent. The suggested U-Net with attention mechanisms demonstrates significant improvements in flood mapping accuracy through thorough testing and comparison with established methodologies, offering a reliable and effective solution for reducing flood-related risks and supporting disaster response activities.