*4.4. Discussion*

In previous research on landslide recognition based on deep learning, Ghorbanzadeh used CNN to train and test landslides in the Himalayas and obtained an F1 value of 87.8% and an mIoU value of 78.26% [21]. Liu (2020) proposed to use ResU-Net to identify earthquake landslides in Jiuzhaigou, Sichuan Province, China, and obtained an F1 value of 93.3% and an mIoU value of 87.5% [25]. Ullo (2021) applied Mask R-CNN to landslide recognition in digital images of target hilly areas acquired by drones; and when ResNet101 was used as the backbone network, the obtained F1 value was 97% [28]. Liu (2021) proposed to use an improved Mask R-CNN to identify earthquake landslides in the Jiuzhaigou area of Sichuan Province, China, and obtained an F1 value of 94.5% and an mIoU value of 89.6% [29]. Ghorbanzadeh (2022) obtained an F1 of 84.03% and an mIoU value of 72.49% when using ResU-Net and OBIA for landslide detection in multitemporal Sentinel-2 images [21]. Our proposed PSPNet, using the classification network ResNet50 as the backbone network, achieves a 91.18% mIoU value on the Bijie landslide dataset; however, due to the differences in datasets and evaluation metrics, we cannot compare it with other models; however, according to the current experimental results, the method proposed in this paper is effective for landslide recognition.

Although PSPNet (ResNet50) achieves good results in landslide identification, it still has some shortcomings. For example, its segmentation of landslide boundaries still needs further improvement. Landslide images are different from traditional remote sensing images. In addition to the information of the images themselves, remote sensing images also contain rich geological information. For example, a digital elevation model (DEM) can reflect local terrain features at a specific resolution. Therefore, we should further combine deep learning with remote sensing to maximize the role of remote sensing data. If the DEM data and remote sensing images are fused, we can obtain the local terrain information of the landslide from the DEM, which will help the model to improve the segmentation accuracy of the landslide boundary.

At present, the automatic identification of landslides based on deep learning still presents research challenges and problems to be solved. For example, the scarcity of open source code for landslide identification research and the lack of high-resolution public landslide remote sensing image datasets and validation areas have brought great difficulties to such research. At the same time, the further improvement in landslide identification accuracy remains to be explored. Given these problems, we need to continue research on automatic landslide identification. In terms of datasets, we will try to integrate the DEM data into remote sensing images so that the datasets contain more information about landslides, thereby improving the accuracy of landslide identification. In terms of models, we will further discuss the influence of model structure on landslide identification and then improve the model to improve the effect of landslide identification.

#### **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

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.

**Author Contributions:** 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.

**Funding:** 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.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** 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.

#### **References**

