## 4 Accurate Recognition of Landslides Based on Semi-Supervised Generative Adversarial Network
### Understanding and Analysis of Semi-Supervised Generative Adversarial Processes  
To compare and analyze the learning performance of different features in the model and select appropriate learning samples, this paper separately feeds the landslide influencing factors sample set, Sentinel-2A optical remote sensing sample set, and the joint sample set combining influencing factors and Sentinel-2A data into the semi-supervised generative adversarial network (SSGAN). It evaluates the model's ability to complete landslide recognition tasks through training with different data inputs. In this comparative study, all model hyperparameters and the number of iterations are kept constant, ensuring a fair evaluation. The experiments were conducted in Python using the TensorFlow framework on a host system equipped with an Intel Xeon Silver 4214 processor and NVIDIA Quadro P2200 graphics card (NVIDIA, Santa Clara, CA, USA). Regarding the discriminator, Figure 5 illustrates the loss function curve during the discriminator's training process. The study decomposes the total loss of the SSGAN discriminator, which is designed for N + 1 class classification, into three parts: real sample classification loss, unlabeled sample true and false loss, and generated sample true and false loss. This breakdown allows for a more in-depth understanding of the discriminator's performance in different functions. When training with the influencing factor sample set, the true and false loss for generated and unlabeled samples stabilizes after a small number of iterations, indicating that the model quickly recognizes true and false samples. However, the overall loss function remains relatively high. This might be attributed to a high learning rate for the generator and the relatively simple distribution of influencing factor data, resulting in rapid convergence for true and false judgments and a continuous decrease in the model's classification loss for true labeled samples. After 100 iterations, the loss for true labeled samples reaches approximately 0.276, indicating improved classification performance. In contrast, when using optical image datasets, the loss in determining the authenticity of generated and unlabeled samples fluctuates significantly. Initially, there is a sharp decrease, followed by a continuous upward trend. This behavior may be due to the high resolution of optical images, which introduces more image noise and a complex feature distribution. This complexity can lead to generator instability, making it challenging for the discriminator to accurately distinguish between samples generated by the generator and labeled samples. Consequently, the model struggles to correctly interpret environmental features, resulting in suboptimal classification performance for labeled samples. Figure 4: Production strategy of sliding cutting sample. For the joint sample set, the model effectively mitigates the issues encountered in optical image training. The judgment of true and false images remains relatively stable, and the loss, although still high, stays below 0.6 overall, which falls within an acceptable range for deep learning model training. Notably, the classification loss for labeled samples is significantly reduced compared to using only influencing factor features, reaching around 0.221 after 100 iterations. These results indicate that the model's performance is promising and will be valuable for landslide classification tasks. In terms of generators, this paper sets fixed random noise seeds during the training process to output the generator generated image results after a specific round of iterations. Some of the results are shown in Figure 6. On three types of datasets, the generator gradually generates results similar to the real data distribution as the number of iterations increases; however, with slight differences. From a more intuitive point of view, using optical image datasets to generate results with more noise is consistent with the discriminator training curve.