Flood Mapping

Flood mapping aims to create accurate representations of flooded areas, crucial for disaster response and mitigation. Current research emphasizes developing high-resolution probabilistic maps using advanced techniques like generative adversarial networks (GANs) to synthesize large datasets from limited historical data, and leveraging satellite imagery (SAR and optical) with deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs like LSTMs), for automated flood detection and depth estimation. These advancements improve the speed and accuracy of flood mapping, informing better emergency response, infrastructure planning, and risk assessment, particularly in urban areas where traditional methods are less effective.