This thesis analyzes the application of semantic segmentation models with different approaches and aims to do a model selection and find the best optimal model with a grid search. The research focuses applying semantic segmentation on main component of landslide containment devices that are mesh and wire. Then it gives brief introduction to researches that are done so far related to semantic segmentation and explains Neural networks and how deep learning models are implemented for semantic segmentation problems. Then the data set is analyzed and exploited, as well as the necessary pre-processing steps to prepare the data for model training. Pre-processing phase includes data annotation, creating different data-sets with and without data augmentation techniques employed and data splitting for generating different separate data for different purposes such that training, validation and testing.