DeepExt: A Convolution Neural Network for Road Extraction using RGB images captured by UAV

In this paper, we propose automatic road extraction using Unmanned Aerial Vehicle (UAV) based Remote Sensing data. Road extraction using UAV data is very useful in traffic management, city planning, GPS based applications, etc. Deep learning techniques namely, Fully Convolutional Network (FCN) and conditional Generative Adversarial Networks (GAN) are used to extract roads from a UAV dataset available in the literature. FCN performs semantic segmentation on the image whereas the GAN generates output images from the model it learns. The results demonstrate the efficiency of the deep learning methods for the task of road extraction.