This paper proposes an efficient degradation model addressing the Space Varying Motion Blur (SVMB) challenge in semantic image segmentation. SVMB distorts object boundaries in motion-captured images, and current State-of-the-Art (SotA) Deep Learning (DL) models require annotated datasets containing SVMB degradation to handle such issues. However, annotating acquired blurred images is often impractical, since the original information is heavily distorted. This work presents a simple and effective technique for generating synthetic SVMB image segmentation data using the Cityscapes benchmark dataset. We leverage the ground truth annotations from the dataset and the Connected Components Algorithm (CCA) to separate the foreground object information. Our experiments demonstrate that U-Net, trained on our SVMB image segmentation dataset augmented with the original Cityscapes dataset, demonstrates superior performance in segmenting synthetic and real-captured blurred image data.