Abstract:
Accurate and up-to-date road maps are crucial for numerous applications such as urban planning, automatic
vehicle navigation systems, and traffic monitoring systems. However, even in the high resolutions remote sensing
images, the background and roads look similar due to the occlusion of trees and buildings, and it is difficult to
accurately segment the road network from complex background images. In this research paper, an algorithm
based on deep learning was proposed to segment road networks from remote sensing images. This semantic
segmentation algorithm was developed with a modified UNet. Because of the lower availability of remote sensing
images for semantic segmentation, the data augmentation method was used. Initially, the semantic segmentation
network was trained by a large number of training samples using traditional UNet architecture. After then, the
number of training samples is reduced gradually, and measures the performance of a traditional UNet model.
This basic UNet model gives better results in the form of accuracy, IOU, DICE score, and visualization of the
image for the 362 training samples. The idea here is to simply extract road data from remote sensing images. As
a result, unlike traditional UNet, there is no need for a deeper neural network encoder-decoder structure. Hence,
the number of convolutional layers in the modified UNet is lower than that in the standard UNet. Therefore, the
complexity of the deep learning architecture and the training time required by the road network model was
reduced. The model performance measured by the intersection over union (IOU) was 93.71% and the average
segmentation time of a single image was 0.28 sec. The results showed that the modified UNet could efficiently
segment road networks from remote sensing images with identical backgrounds. It can be used under various
situations.
