# Automatic Identification of Landslides Based on Deep Learning
## 4 Results and Discussion
### Experimental Results and Analysis
Landslide III: There is a chasm in identifying the landslide by U-Net (VGG); the color of the chasm part is darker, so the background color is closer, which is caused by the new vegetation growing on the landslide, the model cannot recognize this and thus identifies errors. The situation of DeepLab v3+ (Xception) is just the opposite. Although it can distinguish landslides from vegetation, it cannot distinguish the features between landslides and roads. DeepLabv3+ (MobileNet), U-Net (ResNet50), PSPNet (MobileNet) and PSPNet (ResNet50) can identify landslides more accurately; however, their sensitivities to landslide boundaries vary. Landslide IV: Landslide IV has been formed for a long time, the landslide has been covered with vegetation, and the entire landslide is green. Because its landslide characteristics are not prominent, they are not easy to separate. This type of landslide is too complex for the model to recognize. Fortunately, according to the recognition effect of these models on Landslide IV, the model can distinguish the landslide from the features of the surrounding vegetation. However, the segmentation effect of the boundary is not accurate enough. We evaluated the model using the metrics in Section 3.5, and the evaluation results are shown in Table 5. Observing the recall index and precision index values, we found that the recall rate of all models is higher than the precision; this means that these models can identify real landslide pixels but also identify many non-landslide pixels as landslide pixels. It can also be seen from the analysis of Figure 6 that when identifying Landslide I and Landslide II, U-Net (VGG), DeepLabv3+ (Xception) and U-Net (ResNet50) easily confuse other objects with similar colors and shapes to landslides. In Table 5, mIoU values are used to evaluate model performance comprehensively. Among these pretrained models, PSPNet (ResNet50) produced the best landslide recognition effect, with an mIoU value of 91.18%, and obtained the highest precision index (93.76%); this means that the model has a good effect on the recognition of landslide pixels. Followed by PSPNet (MobileNet) and U-Net (ResNet50), the mIoU values of which are 89.11% and 88.75%, respectively; PSPNet (MobileNet) obtained the highest recall index (97.39%), which means that the model can identify most of the landslide pixels. U-Net (VGG) has the worst landslide recognition effect, with a mIoU of 81.64% and a recall and precision of 89.34% and 89.22%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **mIoU** & **Recall** & **Precision** \\ \hline U-Net & VGG & 81.64\% & 89.34\% & 89.22\% \\ DeepLab v3+ & Xception & 86.15\% & 92.26\% & 92.20\% \\ DeepLab v3+ & MobileNet & 87.06\% & 94.06\% & 91.64\% \\ U-Net & ResNet50 & 88.75\% & 96.15\% & 91.82\% \\ PSPNet & MobileNet & 89.11\% & 97.39\% & 92.61\% \\
**PSPNet** & **ResNet50** & **91.18\%** & **96.9**\% & **93.76**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of the suggested model. Numbers in bold represent the best model for identifying landslides (with mIoU metric as the final criterion). Below, we combine the chart to discuss the recognition effect of PSPNet on landslides when MobileNet and ResNet50 are used as the backbone network, respectively. As shown in Table 6, we used precision, recall and IoU to evaluate the model's ability to recognize landslide and background pixels, respectively, when using two different backbone networks. P is the abbreviation for precision, R is the abbreviation for recall and IoU represents intersection over union. In identifying background pixels, when ResNet50 is used as the backbone network, the IoU value is 97.76%, which is 14.79% higher than when MobileNet is used as the backbone network; this can be seen intuitively from the landslide prediction map. In Figure 6, the blue boxes mark the parts of PSPNet (ResNet50) and PSPNet (MobileNet) that misidentify the background pixels as landslide pixels.