Application of U-Net and Auto-Encoder to the Road/Non-road Classification of Aerial Imagery in Urban Environments.

One of the challenges in extracting road network from aerial images is an enormous amount of different cartographic features interacting with each other. This paper presents a methodology to detect the road network from aerial images. The methodology applies a Deep Learning (DL) architecture named U-Net and a fully convolutional Auto-Encoder for comparison. High-resolution RGB images of an urban area were obtained from a conventional photogrammetric mission. The experiments show that both architectures achieve satisfactory results for detecting road network while maintaining low inference time once DL networks are trained.