Abstract
Effective landslide monitoring is essential for mitigating risks to infrastructure and communities, particularly in geologically unstable regions. Traditional monitoring methods, such as ground surveys and visual inspections, are time-intensive and lack early detection capabilities. To address these limitations, this study employs feature fusion and enhanced Deep Convolutional Neural Networks (DCNNs) for landslide detection. The model is built upon a fine-tuned, pre-trained VGG16 architecture, adapted to a new landslide dataset. Key modifications include the integration of a spatial attention mechanism, optimized learning rate schedules, attention-based Global Average Pooling (GAP), and the Lookahead Adam optimizer, all aimed at improving feature extraction, model convergence, and generalization. Experimental results demonstrate that the proposed approach achieves high accuracy, with performance ranging from 90% to 96% across different datasets and training iterations. Using the Kaggle Landslide Dataset, the model attained a training accuracy of 93%, with validation and testing accuracies of 95.2% and 95.8%, respectively. Comparable results were observed with the NASA Landslide Inventory, confirming the robustness of the method. The findings highlight the potential of DCNN-based models, augmented with attention mechanisms, as a reliable and efficient tool for landslide monitoring, significantly outperforming conventional assessment methods.
Data availability
The datasets used in this study are publicly available in Kaggle for academic and research purposes. The datasets used during the current study are available from the corresponding author on reasonable request.
Code availability
The custom code and mathematical algorithms developed in this study are publicly available on GitHub. Readers can access the code at the following link: https://github.com/skbsangeetha/Landslide-Detection/.
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Acknowledgements
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/353/46. This Research is supported by Princess Nourah bint Abdulrahman university by Researchers Supporting Project number PNURSP2026R195, Princess Nourah bint Abdulrahman university, Riyadh, Saudi Arabia.
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S.K.B, S., N, K., M R, P. et al. Attention driven deep convolutional network with optimized learning for accurate landslide detection and monitoring. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36737-2
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DOI: https://doi.org/10.1038/s41598-026-36737-2
