Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Developing landslide susceptibility modeling is essential for detecting landslide-prone areas. Recently, deep learning theories and methods have been investigated in landslide modeling. However, their generalization is hindered because of the limited size of landslide data. In the present study, a novel deep learning-based landslide susceptibility assessment method named deep random neural network (DRNN) is proposed. In DRNN, a random mechanism is constructed to drop network layers and nodes randomly during landslide modeling. We take the Lushui area (Southwest China) as the case and select 12 landslide conditioning factors to perform landslide modeling. The performance evaluation results show that our method achieves desirable generalization performance (Kappa = 0.829) and outperforms other network models such as the convolution neural network (Kappa = 0.767), deep feedforward neural network (Kappa = 0.731), and Adaboost-based artificial neural network (Kappa = 0.732). Moreover, the robustness test shows the advantage of our DRNN, which is insensitive to variations in training data size. Our method yields an accuracy higher than 85% when the training data size stands at only 10%. The results demonstrate the effectiveness of the proposed landslide modeling method in enhancing generalization. The proposed DRNN produces accurate results in terms of delineating landslide-prone areas and shows promising applications.