DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers.. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder-decoder based segmentation models. Different state-of-the-art segmentation models like U-Net, V-Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre-trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U-Net segmentation process. The proposed model has been trained, validated as well as tested using the high-resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at Imteaz1998/DeepRoadNet. A robust model named DeepRoadNet is proposed by utilizing a pre-trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder. Before that, existing state-of-the-art segmentation models such as U-Net, V-Net, ResUNet, and SegNet are also simulated for road map detection from aerial images . The extensive result analysis of all experiments presents the supremacy of our proposed DeepRoadNet model.image