Subimages-Based Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network

Machichi, Mouad Alami; Saadane, Abderrahim; Guth, Peter L.

doi:10.1007/978-3-030-80458-9_10

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…In contrast, deep learning has been recognized as better at converting low-level feature representations into deeper-level feature representations through multi-layer neural network training to further explore the distributed features of data; hence, this method has been widely used in landslide susceptibility mapping in recent years [20,21]. As one of the most representative methods in deep learning, convolutional neural networks (CNN) can better simulate the formation of landslide hazards with their powerful expressive learning capability to accurately predict potential landslide risk [22,23]. Wang et al [24] applied CNN models to landslide susceptibility evaluation work by constructing CNN-1D, CNN-2D, and CNN-3D models for feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

A Combination of Deep Autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation

Wang

Liu

et al. 2023

Remote Sensing

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Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed; specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction.

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