  In this paper, we propose an efficient architecture for semantic image
segmentation using the depth-to-space (D2S) operation. Our D2S model is
comprised of a standard CNN encoder followed by a depth-to-space reordering of
the final convolutional feature maps. Our approach eliminates the decoder
portion of traditional encoder-decoder segmentation models and reduces the
amount of computation almost by half. As a participant of the DeepGlobe Road
Extraction competition, we evaluate our models on the corresponding road
segmentation dataset. Our highly efficient D2S models exhibit comparable
performance to standard segmentation models with much lower computational cost.
