@Article{cmc.2025.072550,
AUTHOR = {Md Minhazul Islam, Yunfei Yin, Md Tanvir Islam, Zheng Yuan, Argho Dey},
TITLE = {A Dual-Stream Framework for Landslide Segmentation with Cross-Attention Enhancement and Gated Multimodal Fusion},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v86n3/65473},
ISSN = {1546-2226},
ABSTRACT = {Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient context modeling and robust feature exchange between modalities. The decoder captures long-range context at deeper levels using lightweight cross-attention and refines spatial details at shallower levels through attention-gated skip fusion, enabling precise boundary delineation and fewer false positives. The gated fusion further enhances multimodal integration of optical and topographical cues, and the deep supervision stabilizes training and improves generalization. Moreover, to mitigate checkerboard artifacts, a learnable sub-pixel upsampling is devised to replace the traditional transposed convolution. Despite its compact design with fewer parameters, the model consistently outperforms state-of-the-art baselines. Experiments on two benchmark datasets, Landslide4Sense and Bijie, confirm the effectiveness of the framework. On the Bijie dataset, it achieves an F1-score of 0.9110 and an intersection over union (IoU) of 0.8839. These results highlight its potential for accurate large-scale landslide inventory mapping and real-time disaster response. The implementation is publicly available at <a href="https://github.com/mishaown/DiGATe-UNet-LandSlide-Segmentation" target="_blank">https://github.com/mishaown/DiGATe-UNet-LandSlide-Segmentation</a> (accessed on 3 November 2025).},
DOI = {10.32604/cmc.2025.072550}
}