Accurate extraction of water bodies from remote sensing imagery is essential for environmental monitoring, water resource management, and hydrological applications. However, the performance of deep learning models for water segmentation depends on the availability of representative datasets that capture diverse environmental and spectral conditions, particularly in tropical and semi-arid regions that remain underrepresented in existing datasets. This study presents the Pernambuco Waterbody Dataset (PWD), a multi-source dataset comprising aerial and satellite remote sensing imagery for water body segmentation. The dataset covers the state of Pernambuco, Brazil, including tropical and semi-arid environments associated with the Atlantic Forest and Caatinga biomes. The dataset includes high-resolution aerial imagery (0.5 m) from the Pernambuco Tridimensional Program (PE3D) and Sentinel-2A imagery (10 m), with manually annotated water bodies generated by cartographic specialists. The dataset was constructed through data acquisition, preprocessing, manual annotation, mask generation, patch extraction (512 × 512 pixels), and division into training, validation, and test subsets. The first version includes 51,743 aerial patches and 15,321 Sentinel-2A patches. To validate the dataset, U-Net, U-Net++, and DeepLabV3+ architectures with ResNet and EfficientNet backbones were evaluated using Recall, Precision, F1-score, and IoU metrics. The best performance was achieved by U-Net++ (ResNet34) for aerial imagery (IoU 0.946) and U-Net (ResNet34) for Sentinel-2A imagery (IoU 0.871). Overall, the proposed dataset provides a robust benchmark for advancing deep learning-based water body extraction using multi-source remote sensing data.