## 1 Introduction  
Landslide, as one of the most destructive natural disasters, is caused by the combination of natural and human factors [1; 2; 3; 4]. The essence of landslide is described as the movement process of earning material sliding off the slope due to gravity, which seriously threatens the safety of life and property [5; 6]. This type of disaster not only gives rise to the direct damage of living facilities, but also leads to the depletion of land resources [7; 8]. In recent decades, a large amount of sloping land has been reclaimed in northeast China, resulting in frequent mountain disasters. Therefore, managing and evaluating the future location of landslides are particularly important in response to such threats. At present, LSM is an attractive way to evade the risk of this disaster [9; 10] as the factors that triggered landslides in the past may continue to induce landslides in the future. The performance of LSM depends on the fitting effect of the model and the quality of the input data [11; 12]. The refinement of algorithms has provided an effective tool for the research of susceptibility, and machine learning models have been widely devoted to constructing functional mappings between variables and landslide susceptibility [13; 14] including logistic regression (LR) [15], radial basis function (RBF) [16], artificial neural network (ANN) [17], random forest (RF) [18], decision tree (DT) [19], regression tree (RT) [20], and support vector machines (SVM) [21]. Compared with the subjective and heuristic models, the machine learning models can successfully handle non-linear data with different scales in the fields of remote sensing, disaster mitigation and wildfire protection [22; 23]. However, these models can be considered as shallow learning structures with only one or zero hidden layers. A large number of shortcomings exist in these models such as limited training time, unstable convergence, local optimal, and so on [24]. In recent years, deep learning (DL), as an attractive framework, has motivated a trend of unprecedented advancement in susceptibility assessment [25]. DL has significant advantages over traditional models: The ability to build advanced features encourages the discovery of the deepest connection between the parameters, which generally obtain a robust performance for nonlinear processing [26; 27]. The latest research has revealed that DNN processed high learning potential in landslide susceptibility assessment with different sampling strategies [28]. The DNN models with multiple optimization algorithms such as stochastic gradient descent (SGD), root mean square propagation (RMSProp), and adaptive moment optimization (Adam) have been compared with traditional machine learning models [29], and their excellent performance and applicability have been confirmed for landslide susceptibility. Nevertheless, in geospatial analysis, the amounts of corresponding labeled samples (landslide or non-landslide events) are still limited and are difficult to collect compared with the huge study area. Particularly for deep learning, the parameters need to be supported by a large number of labeled samples [30]. It is common that the number of disasters usually fails to meet the requirements of modeling. This problem may lead to deviation and over-fitting, which may bring about inestimable errors in the prediction [31; 32]. Although the regularization technique and reducing the dimension of feature can screen valuable information to some extent, the expansion of sample data is still the most effective means to enhance training [33; 34]. With the development of remote sensing (RS) and geographic information system (GIS) technologies, high-resolution digital elevation models (DEM) and engineering data are more easily obtained. A large number of unlabeled sites are not utilized, which are rich in location and geographic information [35]. Meanwhile, unsupervised learning using only unlabeled data can also implement LSM due to their advantages of strong efficiency and scalability for training [36].