# Automatic Identification of Landslides Based on Deep Learning
## 4 Results and Discussion
### Experimental Results and Analysis  
Pretrained models obtained by the experiment were used to predict the test set. The prediction results show that these models can effectively identify landslides. At the same time, according to the experimental results, we conclude that the PSPNet model using ResNet50 as the backbone network has the best recognition effect. We will discuss the results first from the perspective of image recognition and then from the perspective of index evaluation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Parameter Values** \\ \hline input\_shape & [473; 473] \\ classes & landslide, background \\ freeze\_Train & True \\ pretrained weights & True \\ datasets used for pre-training & VOC data set \\ Init\_Epoch & 0 \\ downsample\_factor & 8 \\ freeze\_epoch & 50 \\ unfreeze\_epoch & 100 \\ freeze\_learning\_rate & \(10^{-4}\) \\ freeze\_batch\_size & 8 \\ unfreeze\_batch\_size & 4 \\ unfreeze\_learning\_rate & \(10^{-5}\) \\ focal\_loss & True \\ dice\_loss & True \\ eager pattern & False \\ aux\_branch & False \\ early\_stopping & True \\ num\_workers & 1 \\ cls\_weights & np.array([1, 2], np.float32) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters for our models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Backbone** \\ \hline U-Net & VGG \\ U-Net & ResNet50 \\ DeepLab v3+ & MobileNet \\ DeepLab v3+ & Xception \\ PSPNet & MobileNet \\ PSPNet & ResNet50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The backbone network selection of the models. A total of 51 remote sensing images of landslides were included in the test set; all of the images were from the eastern part of the Qinghai-Tibet Plateau in Bijie City, Guizhou Province, China. As shown in Figure 4, we show some landslide images in the test set, and none of the images in the test set participated in the training, through which we evaluated the performance of each model. We have selected some of these samples for analysis, as shown in Figure 5. The figure includes four samples of landslide images that occurred in different places, marked as Landslide I, Landslide II, Landslide III, and Landslide IV. Among them, I, II, and III are new landslides with various characteristics and shapes of landslides, and IV is an old landslide with inconspicuous characteristics of landslides. In addition, the figure also includes the label file of the landslide, which was used for comparison with the predicted map. We use the pretrained model to obtain the predicted map; we will analyze the predicted maps of the four landslide samples separately. Figure 4: Examples of landslide samples in the test set, all from Bijie, Guizhou, China. Figure 5: Model prediction of landslide results. Horizontally, they are Landslide I, Landslide II, Landslide III and Landslide IV. Vertically, they are the landslide image, label map (for comparison), U-Net (VGG) recognition map, DeepLabv3+ (Xception) recognition map, DeepLab v3+ (MobileNet) recognition map, U-Net (ResNet50), the recognition map of PSPNet (MobileNet) and the recognition map of PSPNet (ResNet50). Landslide I: U-Net (VGG) has a high error rate in identifying landslide images; it identifies the background associated with the landslide color as a landslide. However, the majority of these environments are land and cultivated land. There are 15 additional prediction maps for the same situation. DeepLab v3+ (Xception) is similar to U-Net (VGG). DeepLab v3+ (MobileNet), U-Net (ResNet50), PSPNet (MobileNet), and PSPNet (ResNet50) can more accurately identify landslides; in contrast, PSPNet (ResNet50) has better performance in recognizing landslides. Landslide II: U-Net (VGG) and DeepLab v3+ (MobileNet) cannot fully identify landslides, so the effect is poor. DeepLabv3+ (Xception) recognizes roads with similar colors as landslides. U-Net (ResNet50) recognizes other objects with similar shapes to landslides as landslides and cannot distinguish the landslide from other content in the background. PSPNet (MobileNet) and PSPNet (ResNet50) perform better.