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Deep Learning Techniques Applied for Road Segmentation

In this paper we investigate state of the art deep learning model topologies applied to the problem of semantic segmentation, particularly for extracting road segments in satellite images. In our experiments we used Pan-Sharpened RGB input data from the SpaceNet Roads Dataset, analyzed U-Net, SegNet and ResNet model performance and investigate how different loss functions affect the model performance.