 A novel end-to-end change detection method is proposed using an effective encoder-de-coder architecture for semantic segmentation named Unet++, which learns change maps from scratch using available annotated data sets and outperforms other state-of-the-art CD methods on very high resolution satellite image data sets. This article was authored by Dai Fengpeng, Yongjun Zhang, and Haiyang Guan.