## 3 Methods for Landslide Identification
### Semi-Supervised Generative Adversarial Network Model Construction  
Drawing from the concepts of semi-supervised learning and GAN theory discussed earlier, this paper introduces the development of a semi-supervised generative adversarial network (SSGAN) for the task of landslide recognition in the primary urban area of Lanzhou City. SSGAN is a semi-supervised extension of the traditional GAN framework, designed to enhance the model's classification accuracy when there is an insufficient number of labeled samples. SSGANs have demonstrated remarkable performance on various datasets in multiple studies since their inception. For instance, [PERSON] et al. [43] employed a SSGAN to classify imbalanced samples, achieving an impressive accuracy rate of up to 97.52%. [PERSON] et al. [44] effectively utilized a SSGAN to compensate for missing values in multivariate time series data. On the CIFAR-10 dataset, a SSGAN improved classification accuracy by 1-2% when compared to other semi-supervised learning methods and traditional GAN approaches. Thus, SSGANs have proven their adaptability across various semi-supervised classification tasks and exhibits substantial potential for further development. Notably, there has been no prior research applying a SSGAN to landslide recognition tasks that also face sample limitations. The core concept of a SSGAN involves an expansion of the discriminator within a conventional GAN network, transforming an N-class classification problem into an N + 1 classification problem, with the additional class corresponding to generated pseudo-images. SSGAN seamlessly integrates both labeled and unlabeled samples as input. For labeled samples, the discriminator meticulously assesses the probability of belonging to one of the N predefined categories. In the case of unlabeled samples, the discriminator evaluates the likelihood of being an actual image from the overall N class. Regarding samples generated by the generator, the discriminator determines the probability of being a false image belonging to the N + 1 class. This configuration introduces a secondary competition between unlabeled samples and generated samples, enhancing the model's comprehension of the real sample distribution and resulting in more precise classification outcomes. Building upon these principles, this paper has constructed an SSGAN model tailored to the task of landslide recognition within the study area. The specific model structure is illustrated in Figure 3.  
Within the model's discriminator component, the input data consists of generator-generated samples, samples that have been labeled as landslides and non-landslides, as well as unlabeled samples. The structure of the discriminator network adheres to the foundational feed-forward deep convolutional neural network (CNN) architecture, which is adept at extracting image features and reducing spatial dimensions. A CNN possesses robust feature learning capabilities and is widely employed in various domains, including pattern recognition and image classification [45]. A CNN comprises several key functional layers, including the convolutional layer, pooling layer, and fully connected layer. Of these, the convolutional layer is the cornerstone of a CNN network. It aggregates information through convolutional kernel operations across both spatial and feature dimensions, allowing it to capture feature details in a localized perceptual manner. In the output of the convolution operation, each point corresponds to a specific portion of the original image, referred to as the receptive field. During the forward propagation process of a CNN, as each convolutional layer reduces the size of its feature map, the amount of information contained in the feature representation gradually increases, accompanied by an expansion of the receptive field. The values of the convolutional kernel are updated during backpropagation, and multiple convolutional kernels are employed in each convolutional layer to capture diverse texture and edge features.