U-Net++ and ConvLSTM based Semantic Event Stream Processing for Real-Time Flood Monitoring

Currently, Floods are the most popular and devastating natural hazards, and their effects on economic stability and social well-being are continuously increasing. Recent methods have focused on fast and accurate detection of submerged regions to improve emergency planning and damage estimation in spatial and temporal dimensions. Satellite Multispectral Images have limited spectral bands and low spatial resolution, which restrict the depth of analysis. Image processing methods use intensity-hue-saturation and statistical features to enhance the spatial and spectral data before segmentation and classification. The proposed UConvFloodNet starts by removing noise for sharper inputs using Wiener filtering, and increases the spatial contrast by converting pixel intensities. With these enhanced images, U-Net++ segments key areas such as flood zones, water bodies, and land covers and tracks how flooding evolves over time, and ConvLSTM adds a temporal layer, capturing changes frame by frame. Together, these steps form a streamlined outline for real-time flood monitoring, which combines image enhancement, spatial enhancement, precise segmentation, and dynamic tracking. The UConvFloodNet performs an Accuracy of 99.31 and 99.06 for Sen1Floods11 and S1GFloods datasets which is better than existing Compact Convolutional Tokenizer integrating U-Net and Vision Transformer (CCT-U-ViT).