Accurate Road Detection from Satellite Images Using Modified U-net

Abstract

In this paper, we present an accurate neural network algorithm to detect roads in satellite images. Based on convolutional neural networks, from a 6-channel image, this model is able to transfer the road structure to the output using both the U-net and the atrous convolution architecture. To train this model, we introduce a new combination of existing loss functions including the binary cross-entropy and the Jaccard distance to avoid false positive detection and increase binary classification accuracy. In terms of precision, recall, the F-score and accuracy, experiments carried out using the Massachusetts roads dataset, provide better results than state-of-the-art road extraction models.