Detection and segmentation of motorways, railroads and other roads with similar features are significant for comprehension of both low and high resolution synthetic aperture radar (SAR) imagery. Separation of transportation network from other fields or features is important to understand area contained in SAR image (i.e. the road density can inform about characteristic of that area). Standard image processing methods are inadequate to detect multiple linear targets correctly where computer vision, especially deep learning, provides more insight about features for different type of roads which help better discrimination of multiple linear features like roads and railroads. State-of-art deep learning algorithms are proposed as solutions for understanding road characteristics and extraction of multiple roads. In this paper, a method which uses deep convolutional neural network (DeepLabv3+) backbone architecture is proposed to detect road and railways concurrently. Semantic segmentation of roads using SAR imagery is challenging since these images differ as ground sample distance changes with sensor types which creates a setback for establishing dataset for all sensors. Training set contains 3 classes (road, railway, other) with collected signatures from TerraSAR-X Spotlight images for classification. Proposed method shows robust performance when applied to other sensor and results are presented.