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