2019/08/22 by Yooseung Wang, Wang, Yooseung, Junghoon Seo +3 · 1 citation
Computer Science · Engineering · Environmental Science · #Advanced Neural Network Applications #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Remote Sensing and LiDAR Applications
paper · pdf · doi:10.48550/arxiv.1908.08223
openalex publication_date 2019/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing. In this paper, we propose an efficient Non-Local LinkNet with non-local blocks that can grasp relations between global features. This enables each spatial feature point to refer to all other contextual information and results in more accurate road segmentation. In detail, our single model without any post-processing like CRF refinement, performed better than any other published state-of-the-art ensemble model in the official DeepGlobe Challenge. Moreover, our NL-LinkNet beat the D-LinkNet, the winner of the DeepGlobe challenge, with 43 % less parameters, less giga floating-point operations per seconds (GFLOPs) and shorter training convergence time. We also present empirical analyses on the proper usages of non-local blocks for the baseline model.