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Road extraction from very high resolution satellite (VHR) images is one ofthe most important topics in the field of remote sensing. In this paper, wepropose an efficient Non-Local LinkNet with non-local blocks that can grasprelations between global features. This enables each spatial feature point torefer to all other contextual information and results in more accurate roadsegmentation. In detail, our single model without any post-processing like CRFrefinement, performed better than any other published state-of-the-art ensemblemodel in the official DeepGlobe Challenge. Moreover, our NL-LinkNet beat theD-LinkNet, the winner of the DeepGlobe challenge, with 43 \% less parameters,less giga floating-point operations per seconds (GFLOPs) and shorter trainingconvergence time. We also present empirical analyses on the proper usages ofnon-local blocks for the baseline model.

NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations