###### Abstract  
With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km\({}^{2}\), with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou. s 2024 11 June 2024  
methods have emerged as prominent techniques due to their autonomous analysis capabilities for large amounts of data [6]. Methods such as random forests [7], support vector machines [8], logistic regression [9], and others have been applied to landslide identification. However, in complex scenarios or with higher accuracy requirements, traditional machine learning algorithms have difficulty meeting actual needs, and problems such as model overfitting and easy trapping in local optima still exist [10]. Deep learning technology, as a further development of artificial neural networks in machine learning, has also been explored by some scholars using such methods. For instance, [PERSON] [11] employed the Mask Region Convolutional Neural Network (Mask R-CNN) for the automatic identification of loess landslides using Google Earth images. Similarly, [PERSON] et al. [12] used CNNs to landslide recognition in the southern region of Rasuwa District, Nepal, and compared their results with traditional machine learning methods, highlighting the advantages of deep learning in landslide recognition. The training process in deep learning can be categorized into two main types: supervised learning and unsupervised learning. Currently, traditional deep learning methods for landslide identification mostly adopt supervised learning [13; 14], with problems such as insufficient landslide samples, imbalanced samples, and limited accuracy results. While research on landslide identification using unsupervised learning often involves processes that are difficult to understand and explain, it is difficult to estimate learning effects and performance, making it difficult to further optimize [15; 16].