Abstract
Landslides pose a significant threat to human life and infrastructure which urges the need for efficient techniques for identifying and categorising them. The advent of deep segmentation models such as the Segformer has shown a remarkable empirical performance for semantic segmentation tasks on well-known benchmark datasets, such as ADE20k and Cityscapes. Therefore, this research proposes utilising Segformer on the benchmark Chinese Academy of Sciences (CAS) Landslide Dataset, which features high-quality aerial images of areas impacted or prone to landslides. Taking advantage of the multi-scale attention mechanism and long-range dependency modeling characteristics of the Segformer architecture, this research aims to achieve state-of-the-art results for landslide segmentation using aerial images. Experimental results show the advantage of the Segformer model in segmenting landslide areas, with the largest Segformer variant achieving an Intersection over Union (IoU) score of 87.795% on the Unmanned aerial vehicle (UAV) dataset, surpassing the previous state-of-the-art model, Multiscale Feature Fusion and Enhancement Network (MFFENet), by 3.4%. On the Satellite (SAT) dataset, Segformer attained an IoU score of 79.300%, outperforming the previous best model, DeepLabv3+, by 11.163%. For the combined UAV&SAT dataset, Segformer achieved an IoU score of 85.157%, surpassing DeepLabv3+, the best previous model by 5.032%.
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References
Bragagnolo, L., et al.: Convolutional neural networks applied to semantic segmentation of landslide scars. CATENA 201 (2021). https://doi.org/10.1016/j.catena.2021.105189
Chen, L.C., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv (2018)
Cheng, L., Li, J., Duan, P., Wang, M.: A small attentional YOLO model for landslide detection from satellite remote sensing images. Landslides 18(8), 2751–2765 (2021). https://doi.org/10.1007/s10346-021-01694-6
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. arXiv (2016)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv (2021)
Froude, M.J., Petley, D.N.: Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci. 18(8) (2018). https://doi.org/10.5194/nhess-18-2161-2018
Lei, T., et al.: Landslide inventory mapping from bitemporal images using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 16(6) (2019). https://doi.org/10.1109/LGRS.2018.2889307
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. arXiv (2015)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. arXiv (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. arXiv (2015)
Tang, X., et al.: Automatic detection of coseismic landslides using a new transformer method. Remote Sens. 14(12) (2022). https://doi.org/10.3390/rs14122884
Xie, E., et al.: Segformer: Simple and efficient design for semantic segmentation with transformers. arXiv (2021)
Xu, Y., et al.: Cas landslide dataset: a large-scale and multisensor dataset for deep learning-based landslide detection. Sci. Data 11(1) (2024). https://doi.org/10.1038/s41597-023-02847-z
Zhou, B., et al.: Scene parsing through ade20k dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017). https://doi.org/10.1109/CVPR.2017.544
Zhou, W., et al.: Mffenet: multiscale feature fusion and enhancement network for RGB-thermal urban road scene parsing. IEEE Trans. Multimed. 24 (2022). https://doi.org/10.1109/TMM.2021.3086618
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Syed, H.M., Oghaz, M.M., Saheer, L.B. (2025). Semantic Segmentation for Landslide Detection Using Segformer. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XLI. SGAI 2024. Lecture Notes in Computer Science(), vol 15447. Springer, Cham. https://doi.org/10.1007/978-3-031-77918-3_3
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DOI: https://doi.org/10.1007/978-3-031-77918-3_3
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