| Citation: |
Zhiqiang Yang, Wen-wen Qi, Chong Xu, Xiao-yi Shao, 2024. Exploring deep learning for landslide mapping: A comprehensive review, China Geology, 7, 329-349. doi: 10.31035/cg2024032
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Exploring deep learning for landslide mapping: A comprehensive review
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Abstract
A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning. Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors. Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. In this review paper, 77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined. We analyze the structures of different DL networks, discuss on five main application scenarios, and assess both the advancements and limitations of DL in geological hazard analysis. The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence, with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization. Finally, we explored the hindrances of DL in landslide hazard research based on the above research content. Challenges such as black-box operations and sample dependence persist, warranting further theoretical research and future application of DL in landslide detection.
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Zhiqiang Yang, Wen-wen Qi, Chong Xu, Xiao-yi Shao, 2024. Exploring deep learning for landslide mapping: A comprehensive review, China Geology, 7, 329-349. doi: 10.31035/cg2024032
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Figure 1.
Global landslide catalog points collected by NASA (detailed information found in the website
https://maps.nccs.nasa.gov/ . -
Figure 2.
Morphology of landslides in remote sensing images in different regions and corresponding manual labels. Landslides in different areas show significant differences in remote sensing images. a-Coseismic landslides triggered by the 2022 MS 6.2 Luding (Sichuan, China) earthquake; b-2013 rainfall-triggered landslides in Tianshui (Gansu, China); c-Coseismic landslides triggered by the 2018 MW 6.6 Iburi, Japan earthquake; d-Coseismic landslides MS 8.0 Wenchuan (Sichuan, China) earthquake.
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Figure 3.
Literature database information. a–Number of articles published per year from 2015.1-2022.9; b–Name of the journal in which the literature was published.
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Figure 4.
Process of forward propagation of artificial neural networks. All inputs transformed by linear transformation and fed into activation functions converting to nonlinear expressions as outputs.
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Figure 5.
Structure of Multilayer perceptron. Each neuron connected to all neurons in the previous layer, and the final output is two classes characterizing landslide and no-landslide.
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Figure 6.
Typical convolutional neural network, the network implements feature extraction by stacking multiple convolutional layers, then the feature flattening fed into the fully connected layer as a classification result (modified fromHacıefendioğlu K et al., 2021).
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Figure 7.
Typical structure of a fully convolutional network. The feature map restored to the same size as the input in the final by up-sampling to achieve pixel-by-pixel classification.
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Figure 8.
Structure of U-Net. The network has a symmetric structure, the encoder layer achieves feature extraction by continuously deepening and narrowing the feature map, and the decoder layer achieves pixel-by-pixel classification through an inverse process.
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Figure 9.
Structure of residual block. The final output convolved and summed with the original inputs.
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Figure 10.
Structure of DenseNet (modified from (Huang G et al., 2018). The network consists of 5 layers of dense blocks. Each layer takes all preceding feature maps as input.
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Figure 11.
Structure of Mask R-CNN. The final result has three branches including box location, classification, and semantic segmentation.
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Figure 12.
a‒Structure of restricted Boltzmann machine; b‒Deep belief network, network extracts and reconstructs of input features by stacking multiple RBM.
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Figure 13.
Structure of Autoencoder, where the encoder projects the high-dimensional inputs into low-dimensional hidden variables thus learning the most features; the decoder restores the hidden variables to the initial dimensions.
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Figure 14.
A general workflow of deep learning application for landslide analysis.
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Figure 15.
Extraction of landslides in single images based on FCN (modified from Yi YN and Zhang WC, 2020).
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Figure 16.
Deep learning-based change detection, the two-branch network learns separate features for the pre-and post-event images, then stacks the two feature maps (modified from Lv et al, 2020).
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Figure 17.
Landslide localization and delineation using Mask R-CNN, the network achieves pixel-by-pixel segmentation by adding a branch to Faster-RCNN (modified from Ullo S et al., 2021).
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Figure 18.
A general framework of landslide mapping based on unsupervised learning, essentially a process of feature extraction and clustering without the participation of labels (modified from Shahabi H et al., 2021).
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Figure 19.
Landslide mapping combing the OBIA and FCN, the final result fused with the outputs of two independent models (modified from Ghorbanzadeh O et al., 2022).