A Change Detection Reality Check
<|reference_start|>A Change Detection Reality Check: 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.<|reference_end|>
@article{corley2024a,
    title={A Change Detection Reality Check},
    author={Isaac Corley, Caleb Robinson, Anthony Ortiz},
    journal={arXiv preprint arXiv:2402.06994},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06994},
    primaryClass={cs.CV cs.LG}
}