Automated simulation framework for flood risk mapping integrating aerial point clouds and deep learning

In recent years, floods have brought renewed attention and requirement for real-time and city-scaled flood forecasting, due to climate change and urbanization. Flood risk mapping through traditional physics-based modeling methods is often unrealistic for rapid emergency response requirements, because of long model runtime, hydrological information lacking, and terrain change caused by human activity. In this study, an automated simulation framework is proposed by integrating aerial point clouds and deep learning technique that is capable of superior modeling efficiency and analysis accuracy for flood risk mapping. The framework includes four application modules, i.e., data acquisition and preprocessing, point clouds segmentation, digital elevation model (DEM) reconstruction, and hydrodynamics simulation. To more clearly demonstrate the advantages of the proposed automated simulation framework, a case study is conducted in a local area of the South-to-North Water Transfer Project in China. In addition, the efficiency and accuracy of the suggested point cloud segmentation network for large-scale 3D point clouds in basin scenes are discussed in detail by comparison with PointNet and PointNet + + networks.