Extracting road maps from high-resolution satellite imagery using refined DSE-LinkNet

Abstract

Road detection and extraction have gained momentum in recent past years with crucial applications such as urban planning, autonomous driving, automated map update, providing aid to rescue missions, etc. The current methodologies generate the disconnected road segments, cause boundary loss, and also they are incapable of handling the imbalanced class distribution problems. In this paper, we propose a fully convolutional architecture, named as refined DSE-LinkNet, to extract the connected and precise road maps. We use a pre-trained encoder by combining the layers of the two very efficient and light-weight CNN models: DenseNet and SE-Net that makes the proposed model more expressive with faster convergence. We introduce a new module, Fusion block, in our architecture that enhances its precise localisation as well as classification ability by capturing multilevel as well as multiscale features. To address the imbalanced class distribution problem, a new aggregate loss function is proposed by integrating binary cross-entropy, Jaccard coefficient, and Lovasz sigmoid loss functions. The experiments are performed on a publicly available dataset, DeepGlobe Road Extraction Challenge 2018, to show its efficacy over the D-LinkNet, winner of DeepGlobe Challenge 2018, by achieving IoU of 0.69 with lesser number of parameters and better computational complexity.