In this article, we propose a novel local-global dual-stream network (DS-Net) that adaptively captures local and long-range information for the accurate mapping of building rooftops in VHR RS images.  The local branch and the global branch of DS-Net work in a complementary manner to each other with different fields of view on the input image.  Through a well-defined dual-stream architecture, DS-Net learns hierarchical representations for both the local and global branches, and a deep feature sharing strategy is further developed to enforce more collaborative integration of the two branches.  Empirically, the proposed DS-Net achieves competitive or superior performance compared with the current state-of-the-art methods in terms of quantitative measures and visual evaluations.
DS-Net
A Local-Global Dual-Stream Network for Building Extraction From Very-High-Resolution Remote Sensing Images.Buildings constitute one of the most important landscapes in remote sensing (RS) images and have been broadly analyzed in a wide range of applications from urban planning to other socioeconomic studies.  In this article, we propose a novel local-global dual-stream network (DS-Net) that adaptively captures local and long-range information for the accurate mapping of building rooftops in VHR RS images. 
Local-Global Dual-Stream Network
A Local-Global Dual-Stream Network for Building Extraction From Very-High-Resolution Remote Sensing Images.Buildings constitute one of the most important landscapes in remote sensing (RS) images and have been broadly analyzed in a wide range of applications from urban planning to other socioeconomic studies. 
Remote Sensing Images
A Local-Global Dual-Stream Network for Building Extraction From Very-High-Resolution Remote Sensing Images.Buildings constitute one of the most important landscapes in remote sensing (RS) images and have been broadly analyzed in a wide range of applications from urban planning to other socioeconomic studies. 
Very-High-Resolution Remote Sensing Images