## 5 Discussion  
### Global Training vs. Local Training  
The study employed global training data to model large-scale landslide susceptibility in China. To validate the model's robustness, it was trained on local training data, and both the global and local models were subsequently evaluated using local testing data. The results of these tests are illustrated in Figure 13. The findings indicate that the SVM and CNN models trained on local data outperform those trained on global data; however, the opposite is true for the ResNet18 model. This demonstrates that when only a few samples are available, the CNN has a relatively simple structure with fewer parameters and can be trained more efficiently. The SVM is better at handling high-dimensional data and generalizing small-sample data. The ResNet18 architecture is inherently deeper than traditional CNNs, and as the dataset size increases, the advantages of its deeper structure become more pronounced. Specifically, ResNet18 outperforms CNNs in terms of predictive accuracy when trained on large volumes of data. In addition, the results of the testing model on the local areas of the global training models showed that ResNet18 exhibited the best robustness. Regarding the recall and f1-measure metrics, the ResNet18 model utilizing global training data achieved the best performance. Consequently, this study provides a sound and reliable framework for evaluating landslide susceptibility in China, employing the ResNet18 model with global training data. Additionally, this study undertook an examination of the performance of the SVM, CNN, and ResNet18 models, focusing on metrics such as parameters, training time, and f1-measure in the global and local training landslide susceptibility assessments, as shown in Table 4. SVMs and CNNs are known to be efficient and practical choices when the sample size is limited. Conversely, when data resources are ample, ResNet18 is capable of delivering a more precise and robust evaluation of landslide susceptibility. Therefore, landslide susceptibility evaluation models should be selected based on specific application scenarios and data sizes. Based on the ResNet18 model, this study used global training data to assess the landslide susceptibility of China, which not only reflected the advantages of the DL model in processing large-scale data but also provided strong technical support for large-scale landslide assessment in the future. By using this approach, decision makers can be provided with a strong, data-driven basis that can make a significant contribution to improving environmental and regional policy. It also provides important evidence for landslide disaster warning, prevention and mitigation, ensuring that proactive measures are consistent with regional planning and environmental management objectives. Figure 13: Test and comparison of local datasets across various models: (**a**) SVM, (**b**) CNN, and (**c**) ResNet18. In addition to the comparison between the SVM, CNN, and ResNet18 models, it is important to consider the computational trade-offs associated with the ResNet18 model, especially in the context of large-scale landslide susceptibility assessments. Table 4 presents a comparative analysis of key factors, including the training time and f1-measure for both global and local training. While ResNet18 shows superior performance in terms of delivering more precise and robust results, as indicated by its higher f1-measure for global training (0.809), its longer training time (1.25 h) is a key consideration. This factor must be taken into account when deploying this model in real-world scenarios. Despite the increased training time, ResNet18's deep learning architecture offers significant scalability advantages, particularly for large-scale applications. The model's ability to handle vast datasets, such as the global landslide data used to assess landslide susceptibility in China, makes it highly suitable for large-scale, real-time landslide risk assessment tasks.