# Automatic Identification of Landslides Based on Deep Learning
## 5 Conclusions  
In this study, we proposed a deep learning-based research method for the automatic identification of landslides and obtained good results on the Bijie landslide dataset. First, we reconstructed the dataset for semantic segmentation and preprocessed the landslide data  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backbone** & **Evaluate** & **Landslide** & **Background** \\ \hline \multirow{3}{*}{MobileNet} & P & 82.79\% & 97.08\% \\  & R & 97.37\% & 97.41\% \\  & IoU & 81.15\% & 82.97\% \\ \hline \multirow{3}{*}{ResNet50} & P & 88.14\% & 99.41\% \\  & R & 95.47\% & 98.34\% \\ \cline{1-1}  & IoU & 84.6\% & 97.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of PSPNet under different backbone networks. The numbers in bold are the highest IoU values obtained for landslide and background recognition, respectively.  
based on the Bijie landslide dataset. We then used three models: U-Net, DeepLabv3+ and PSPNet, each using two classification networks as the backbone network, and completed training and validation on the Bijie landslide dataset on these models. Finally, we used the experimentally obtained pretrained model for landslide recognition and used mIoU, precision and recall to evaluate the model's recognition effect on landslides. According to the experimental results, we obtained the best recognition effect of the PSPNet model with ResNet50 as the backbone network, with an mIoU of 91.18%. This experimental result shows that it is feasible to detect the automatic identification of landslides by using the deep learning method; simultaneously, our proposed PSPNet method with ResNet50 as the backbone network can effectively identify landslides.  
Based on the above experimental results, we believe that this research will be helpful to landslide relief operations in real life. The automatic identification method can effectively make up for the shortcomings methods, which are time-consuming, labor-intensive and highly dependent on labor; save much time and human resources for emergency rescue work; and reduce the loss of life and property. At the same time, it can help geologists significantly improve their work efficiency and allow them to spend more time on work that requires more geologists. Therefore, this research has significant practical potential.  
In future work, we will contribute to the lack of high-resolution landslide remote sensing image datasets and open source code for landslide identification research. We will try to enrich the landslide data set by adding more factors so that it contains more landslide information for the model to improve the recognition accuracy of landslides further. In terms of models, we will try to explore, for example, the self-attention mechanism is introduced into the model so that the model can learn the characteristics of landslides in a more targeted way, thereby improving its accuracy. In addition, compared with new landslides, the characteristics of old landslides are less obvious, and it is not easy to realize automatic identification. Thus, we will also try to apply the training model of new landslides to the extraction of old landslides through transfer learning, thereby improving the accuracy of deep learning methods in the automatic identification of old landslides.  
Conceptualization, Y.W. and Y.Z.; methodology, S.Y. and Y.W.; software, S.Y., P.W. and X.Z.; validation, S.Y. and P.W.; writing--original draft preparation, S.Y.; writing--review and editing, Y.W., J.M., S.J., Z.W. and K.W.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 41872253 and in part by the GHFUND B of China under Grant ghfund202107021958.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.