Rapid mapping of flood inundation by deep learning-based image super-resolution

# Rapid mapping of flood inundation by deep learning-based image super-resolution # Developer: Wenke Song # The University of Hong Kong # Contact email: [e-mail] # MIT License # Copyright (c) 2024 songwk0924 There are two folders in the compressed file: Trained_model and Test_cases: (1) Trained_model model_d_DenseUnet.pth, for predicting the maximum water depth; model_v_DenseUnet.pth, for predicting the maximum velocity. (2) Test_cases Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3; Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; bathy_mat_5m_0p.csv: Elevation data to create mask layer; Fine_grid_flood_maps (2DSWEs): hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3; velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3; The aforementioned data will be used as input for model prediction (Prediction.py). https://github.com/songwk0924/Flood-inundation-mapping