Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{167132,
        author = {katroth Balakrishna Maruthiram and Dr.G.Venkata Rami Reddy and Munigala Anusha},
        title = {AFDE-Net Building Change Detection Using Attention-Based Feature Differential Enhancement for Satellite Imagery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {279-285},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167132},
        abstract = {Agricultural land the board, building change detection (BCD) utilizing satellite pictures, and GIS information base updates all rely upon it. Notwithstanding, deep learning-based change discovery strategies, which often focus on variety and surface, experience issues with regards to perplexing varieties in building rooftops that copy their environmental elements. Moreover, loss of spatial data from down testing could bring about lopsided result limits and incomplete developments. We present AFDE-Net, a remarkable Siamese organization that joins consideration modules and differential picture qualities with a learnable boundary, to defeat these issues. To decrease the deficiency of spatial data and improve the nature of profound elements in high-layered inputs, AFDE-Net uses anensemble spatial-channel attention fusion (ESCAF) module related to a deep supervision (DS) module. We likewise give EGY-BCD, a novel dataset made to recognize building changes that comprises of high-goal, multitemporal satellite pictures from four Egyptian waterfront and metropolitan areas. Profound learning calculations are tested by EGY-BCD on the grounds that it contains photographs with muddled types of progress, like thick structures with rooftops that imitate their environmental elements. On the EGY-BCD dataset, AFDE-Net performs better compared to different methodologies, with a overall accuracy (OA) of 94.3%, a F1-score of 88.8%, and a mIoU of 86.6%.},
        keywords = {Buildings, Feature extraction, Satellites, Semantics, Remote sensing, Computer architecture, Change detection algorithms},
        month = {August},
        }