The SemanticTHAB dataset is a large-scale dataset designed for semantic segmentation in autonomous driving. It contains 4,750 3D LiDAR point clouds collected from urban environments. The dataset includes labeled point clouds with 20 semantic classes, such as road, car, pedestrian, and building. It provides ground truth annotations for training and evaluating semantic segmentation algorithms, offering a real-world benchmark for 3D scene understanding in self-driving car applications. The dataset is desinged to extent the SemanticKITTI benchmark by scans of a modern high resolution LiDAR sensor (Ouster OS2-128, Rev7).