 Scene classification is an essential conception task used by robotics for understanding the environment.  The deep learning technique has been proved as a great role in the challenging scene understanding application.  Using data augmentation to increase dataset size  Using K-means clustering as a preprocessor for the input dataset  The proposed hydride model is generated by combined two of the deep , deep neural networks as an xception and U-net models.Scene classification is an essential conception task used by robotics for understanding the environment. Like the street scene, the outdoor scene is composed of images with depth that has a greater variety than iconic object images. Image semantic segmentation is an important task for Autonomous driving and M obile robotics applications because it introduces enormous information needed for safe navigation and complex reasoning. This paper provides a model for semantic segmentation of outdoor sense to classify each object in the scene. The proposed network model generates a hybrid model that combines U-NET with Xception networks to work on 2.5 dimensions cityscape dataset, which is used for 3D applications. This process contains two stages. The first is the pre-processing operation on the RGB-D dataset (data Augmentation and k-means cluster). The second stage designed the hybrid model, which achieves a pixel accuracy is 0.7874. The output module is generated using a computer with GPU memory NVIDIA GeForce RTX 2060 6G, programming with python 3.7.