Abstract: Semantic information extraction of remote sensing images is becoming one of the key technologies in urban planning and utilization, land cover survey, disaster change detection and maritime situational awareness. Starting from the intelligent processing requirements of remote sensing images developed from single-source to multi-source, the development status of semantic segmentation of remote sensing images in the era of big data and in the context of deep learning is summarized and analyzed firstly in this paper,including single-source image semantic segmentation, multi-source remote sensing image fusion semantic segmentation and multi-source(homogeneous/ heterogeneous)remote sensing image change detection. Then, on the basis of expounding the main methods, the key technologies of semantic segmentation of multi-source remote sensing images are refined and summarized, including fast semantic segmentation of single-source remote sensing images, accurate registration and fusion of multi-source remote sensing images assisted by semantic information, and intelligent extraction of semantic information based on multi-source remote sensing images. Finally, aiming at the on-orbit processing requirements of multi-source remote sensing images, the technical challenges faced by intelligent integrated information extraction of high-resolution multi-source remote sensing images are summarized,and the future development trend is prospected.