Many challenges face semantic understanding of urban street scenes. Two of the most important challenges are foggy and blurred scenes. In this work we make a comparison between two of the most powerful methods in semantic segmentation. These techniques are DeepLabv3+ and PSPNet which achieve the highest mIoU and approximately close to each other on the Cityscapes dataset testing using both fine and coarse data for training. DeebLabv3+ and PSPNet achieved an accuracy of 82.1 % and 81.2% on Cityscapes test set respectively. Our experimental results discuss the performance of these methods on two of the hardest challenges in semantic segmentation which are foggy and blurred scenes.