## 5 Results and Discussion
### Landslide Recognition Results  
This paper utilized the trained model to conduct supplementary landslide recognition in the main urban area of Lanzhou City. To demonstrate the effectiveness of semi-supervised generative adversarial learning, a comparative study was conducted using a model that only includes the discriminator part of SSGAN. This comparative model takes labeled samples as input and outputs two classification results, while keeping other hyperparameters consistent with SSGAN. After pixel-level recognition, to align with the actual development of landslides, the generated results were clustered and smoothed. The landslide recognition results of the two models are depicted in Figure 8a illustrates that the model without semi-supervised generative adversarial training (GAN) recognized 204 landslides, including constraint recognition results, covering a total area of 10.913 km\({}^{2}\). SSGAN identified 160 landslides in the study area, the minimum landslide area identified is 68 m\({}^{2}\) and the maximum is 0.68 km\({}^{2}\), the total area is 10.328 km\({}^{2}\), which is 3.139 km\({}^{2}\) more than the recognition results of the landslide constraints, as shown in Figure 8b. From Figure 8, it is evident that more small landslides were identified without the adversarial learning model. The SSGAN recognition results align closely with the distribution of the sample landslides, providing more detailed information about the landslide edges, and even capturing some individual landslides that exceed the original landslide areas. Both methods identified more landslides than the original sample. Based on the above accuracy analysis and evaluation indicators, Table 4 presents the quantitative results for two distinct models, Model a represents the result of the traditional network generated by adversarial learning without semi-supervision (GAN), and Model b represents the results obtained by SSGAN used herein:  
Lanzhou City primarily experiences mixed, mid-level, and small loess landslides, with the landslide body composed mainly of loess and secondary loess of various origins. These landslides exhibit spectral and textural characteristics highly similar to the surrounding environment, posing significant challenges to their accurate identification. The method proposed in this paper outperforms the model without semi-supervised adversarial learning across four comprehensive evaluation metrics: precision, F1 score, Kappa coefficient, and MIoU. However, the latter model achieves a higher recall rate of 96.09%. Recall rate measures the detection coverage of the detector on all targets to be detected, and the higher recall rate suggests that the model may be sensitive to data noise, erroneously classifying other ground features as small landslides. This results in a higher recall rate but lower accuracy rate. It is worth noting that the evaluation of a deep learning model should consider several indicators, the synthesis of these indicators furnishes a more holistic appraisal of the model's performance. Based on the five indicators quantified in this study, the model proposed in this paper exhibits a better balance and overall performance. When \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** & **Kappa Coefficient** & **MIoU** \\ \hline Model a & 0.795 & 0.961 & 0.860 & 0.858 & 0.887 \\ (GAN) & 0.829 & 0.952 & 0.879 & 0.878 & 0.899 \\ Model b (SSGAN) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of landslide recognition and evaluation indicators. Figure 8: Comparison of accurate landslide recognition results in the main urban area of Lanzhou City: (**a**) GAN; (**b**) SSGAN. Pink represents the accurate landslide recognition results by GAN, green represents the accurate landslide recognition results by SSGAN.