  In recent years, there has been an explosion of proposed change detection
deep learning architectures in the remote sensing literature. These approaches
claim to offer state-of-the-art performance on different standard benchmark
datasets. However, has the field truly made significant progress? In this paper
we perform experiments which conclude a simple U-Net segmentation baseline
without training tricks or complicated architectural changes is still a top
performer for the task of change detection.
