{"ID":2839060,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16322","arxiv_id":"2511.16322","title":"ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery","abstract":"Remote sensing change detection (RSCD) aims to identify surface changes from co-registered bi-temporal images. However, many deep learning-based RSCD methods rely solely on change-map annotations and underuse the semantic information in non-changing regions, which limits robustness under illumination variation, off-nadir views, and scarce labels. This article introduces ChangeDINO, an end-to-end multiscale Siamese framework for optical building change detection. The model fuses a lightweight backbone stream with features transferred from a frozen DINOv3, yielding semantic- and context-rich pyramids even on small datasets. A spatial-spectral differential transformer decoder then exploits multi-scale absolute differences as change priors to highlight true building changes and suppress irrelevant responses. Finally, a learnable morphology module refines the upsampled logits to recover clean boundaries. Experiments on four public benchmarks show that ChangeDINO consistently outperforms recent state-of-the-art methods in IoU and F1, and ablation studies confirm the effectiveness of each component. The source code is available at https://github.com/chingheng0808/ChangeDINO.","short_abstract":"Remote sensing change detection (RSCD) aims to identify surface changes from co-registered bi-temporal images. However, many deep learning-based RSCD methods rely solely on change-map annotations and underuse the semantic information in non-changing regions, which limits robustness under illumination variation, off-nad...","url_abs":"https://arxiv.org/abs/2511.16322","url_pdf":"https://arxiv.org/pdf/2511.16322v1","authors":"[\"Ching-Heng Cheng\",\"Chih-Chung Hsu\"]","published":"2025-11-20T12:55:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":606839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2839060,"paper_url":"https://arxiv.org/abs/2511.16322","paper_title":"ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery","repo_url":"https://github.com/chingheng0808/ChangeDINO","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}