| Citation: |
Chang-dong Li, Peng-fei Feng, Xi-hui Jiang, Shuang Zhang, Jie Meng, Bing-chen Li, 2024. Extensive identification of landslide boundaries using remote sensing images and deep learning method, China Geology, 7, 276-289. doi: 10.31035/cg2023148
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Extensive identification of landslide boundaries using remote sensing images and deep learning method
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Abstract
The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue. It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response. Therefore, the Skip Connection DeepLab neural network (SCDnn), a deep learning model based on 770 optical remote sensing images of landslide, is proposed to improve the accuracy of landslide boundary detection. The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features. SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block (ASPC) with a coding structure that reduces model complexity. The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8 and 0.9; while 52 images with MIoU values exceeding 0.9, which exceeds the identification accuracy of existing techniques. This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future investigations and applications in related domains.
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References
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Chang-dong Li, Peng-fei Feng, Xi-hui Jiang, Shuang Zhang, Jie Meng, Bing-chen Li, 2024. Extensive identification of landslide boundaries using remote sensing images and deep learning method, China Geology, 7, 276-289. doi: 10.31035/cg2023148
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Figure 1.
Study area map for the Three Gorges Reservoir, with the landslide images sourced from the research of Ji S et al. (2020).
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Figure 2.
Bijie landslide image and landslide shape mask dataset, with the landslide images sourced from the research of Ji S et al. (2020).
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Figure 3.
Architecture of Skip Connection DeepLab neural network.
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Figure 4.
Mixed Residual and Maximum Pooling Downsampling Block (Legends are same as Fig. 3).
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Figure 5.
Other Component Blocks of SCDnn: a‒Mixed Residual and Convolutional Downsampling Block; b‒Atrous Spatial Pyramid Convolutional Block; c‒Convolution Block; d‒Mixed Residual and Convolutional Block (Legends are same as Fig. 3).
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Figure 6.
Segmented images generated by different network architecture models for precise location identification of landslides, with the landslide images sourced from the research of Ji S et al. (2020).
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Figure 7.
Segmentation results of different models for individual classes of landslide dataset, with the landslide images sourced from the research of Ji S et al. (2020). a‒SSSN and SCDnn models for individual classes of landslide dataset; b‒ResNet50 and SCDnn models for individual classes of landslide dataset.
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Figure 8.
Statistics on the range of individual image’s MIoU values for the segmentation results of the SCDnn model for the landslide dataset.
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Figure 9.
Some of the remote sensing images and shape masks from SCDnn model with segmentation results’ MIoU values between 0.8 and 0.9 and MIoU values larger than 0.9, with the landslide images sourced from the research of Ji S et al. (2020).
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Figure 10.
Some of the remote sensing images and shape masks from SCDnn model with segmentation results’ MIoU values less than 0.6, with the landslide images sourced from the research of Ji S et al. (2020).