Flood Inundation Mapping

This dataset provides simulated flood inundation maps of Abu Dhabi's coast under 174 different shoreline protection scenarios. The maps were produced with a high-fidelity physics-based hydrodynamic simulator under a 0.5-meter sea level rise projection. The details of the hydrodynamic model are reported in [1]. The coastline was partitioned into 17 segments. The input protection scenarios were encoded as 17-dimensional binary vectors, with 1 indicating the placement of containments and 0 otherwise. The output inundation depth maps are named according to protection scenarios. Each map is a 1024x1024 matrix containing the estimated peak water level values (in meters) for selected 12066 locations along the coastline of Abu Dhabi. The mapping from gird indices to the original geographical coordinates of these locations is provided in a separate file (ad_grid_1024.npy). A Jupyter notebook with a sample Python code is included for visualizing the data entries (see Data_Visualization.ipynb). [1] Chow, Aaron C. H., and Jiayun Sun. 2022. "Combining Sea Level Rise Inundation Impacts, Tidal Flooding and Extreme Wind Events along the Abu Dhabi Coastline" Hydrology 9, no. 8: 143. https://doi.org/10.3390/hydrology9080143

The repository hosts the STURM-Flood dataset, an open-access resource designed for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery. The dataset comprises 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles, each of size 128 × 128 pixels at a resolution of 10 meters, along with corresponding water masks covering 60 flood events globally. This curated dataset is optimized for deep learning applications and provides ground-truth data from the Copernicus Emergency Management Service to facilitate robust model development. We invite researchers and developers to utilize this resource for advancing flood mapping techniques in disaster management. For further details on the methodology, results, and implementation, please refer to our study published in Big Earth Data (2096-4471): https://doi.org/10.1080/20964471.2025.2458714 .