Flood Inundation Mapping

Flood inundation mapping aims to create accurate and timely maps of flooded areas, crucial for disaster response and risk assessment. Current research emphasizes developing high-resolution probabilistic maps using various techniques, including generative adversarial networks (GANs) to synthesize training data, deep learning models like U-Net and convolutional neural networks (CNNs) for image analysis, and the integration of multi-modal data sources (e.g., satellite imagery, rainfall data, elevation models). These advancements improve the speed and accuracy of flood mapping, supporting better resource allocation, improved disaster preparedness, and more effective flood mitigation strategies.