Sentinel-2 Surface Water Segmentation Dataset for the Southern Iraqi Marshes (2021)

Dataset Description This dataset contains Sentinel-2 satellite imagery of the Southern Iraqi Marshes, prepared for training and evaluating fully convolutional neural networks (FCNs) for binary semantic segmentation of surface water. The dataset is intended for research and educational use in remote sensing, hydrology, and machine-learning-based water body mapping, particularly for benchmarking deep learning models on medium-resolution multispectral imagery. Data Sources Sentinel-2 Level-2A surface reflectance imagery (Copernicus / ESA), accessed and exported using Google Earth Engine JRC Global Surface Water (GSW) Yearly History dataset for water extent labels Image Preprocessing Sentinel-2 imagery was exported as four 10 m spatial resolution bands: B4 – Red B3 – Green B2 – Blue B8 – Near-Infrared The bands were exported as 8-bit GeoTIFF files and subsequently processed offline. All image–mask pairs were spatially aligned to ensure identical coordinate reference systems, transforms, and pixel grids. Where required, masks were resampled to the image grid using nearest-neighbour reprojection to preserve discrete class labels. Each image–mask pair was tiled into non-overlapping 512 × 512 pixel patches. Image tiles were then min–max normalised on a per-tile basis and stored as floating-point arrays in the range [0, 1]. Final image tiles have shape (512, 512, 4) and dtype float32. Ground-Truth Water Masks Water masks were derived from the JRC Global Surface Water Yearly History product. Exported water layers were binarised such that: Water pixels → 1 Background (non-water) pixels → 0 Masks were saved as NumPy arrays with shape (512, 512, 1) and dtype uint8. This release uses 2021 data only, as configured in the preprocessing pipeline. Dataset Splitting and Balancing To mitigate severe class imbalance commonly present in water segmentation tasks, tiles were categorised based on water coverage fraction: Empty: 0% water Mixed: >0% and