# Automatic Identification of Landslides Based on Deep Learning
## 4 Results and Discussion
### Discussion  
In previous research on landslide recognition based on deep learning, [PERSON] 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]. [PERSON] (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]. [PERSON] (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]. [PERSON] (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]. [PERSON] (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.