Abstract Identifying changes in satellite images is vital for tasks like tracking land cover and land use, evaluating disaster impacts, and conducting military surveillance. Although conventional techniques for detecting changes in multispectral remote sensing data are commonly applied, they often fail to meet the requirements for reliability and precision. Recently, deep learning methods have emerged, providing more accurate and effective solutions for monitoring environmental transformations and urban expansion in satellite imagery. This paper introduces TransSiamUNet, a deep learning architecture that combines Siamese networks, U-Net segmentation, and Vision Transformers (ViT) for high-precision change detection. The model processes paired Sentinel-2 images via a tailored preprocessing pipeline and integrates local and global feature extraction for pixel-level change segmentation. On the OSCD benchmark, TransSiamUNet achieves an accuracy of 0.94, surpassing the Siamese network (0.86), U-Net (0.84), and Siamese+U-Net hybrid (0.91). These results demonstrate the model’s superior capability in detecting fine-grained urban and environmental changes, highlighting its suitability for real-world remote sensing applications. Data availability Data and code used for training, evaluation, and experimentation in this study are available on request. Code availability The code used for training, evaluation, and experimentation in this study is available on request. Materials availability All datasets and code used in this study are available. The Arabic summarization dataset and model implementations can be available on request. References Richards, J. A. Remote sensing digital image analysis: An introduction (Springer, Cham, 2023a). Liu, X., Zhang, H. & Chen, Y. Urban expansion detection using ndbi and remote sensing data: A case study of rapid urbanization. Remote Sensing 15(4), 1123–1138 (2023a). Hughes, L. H., Schmitt, M., Mou, L., Wang, Y. & Zhu, X. X. Identifying corresponding patches in sar and optical images with a pseudo-siamese cnn. IEEE Geoscience and Remote Sensing Letters 15(5), 784–788 (2018). Daudt, R.C., Le Saux, B. & Boulch, A.