# Automatic Identification of Landslides Based on Deep Learning
## 2 Related Work  
Convolutional neural networks (CNN) [12] have achieved great success in the field of image processing because of their nonlinear learning ability [13], driving the rapid development of computer vision [14; 15]. Based on CNN studies, various models have been developed for image classification [16], object detection [17; 18], semantic segmentation [19], etc, where semantic segmentation performs pixel-level segmentation of the images. These models have achieved satisfactory results in traditional vision tasks. Therefore, people have begun to apply these deep learning models to landslide identification in remote sensing images in the past few years. [PERSON] et al. (2019) proposed a constrained deep learning model, applied it to identifying landslides in hyperspectral images, and compared the results with the support vector machine-spectral information divergence-spectral angle matching method. They that the extraction of high-level features by deep learning has great potential for improving the accuracy of landslide identification [20]. [PERSON] et al. (2019) used CNNs for Himalayan landslide identification and compared them with state-of-the-art machine learning methods (artificial neural networks, support vector machines, and random forests); the results show that deep learning is superior to machine learning in landslide identification experiments [21]. [PERSON] et al. (2020) proposed an improved U-Net model that uses ResNet34 blocks for feature extraction and enables landslide identification in Douglas County, south of Portland, Oregon, USA [22]. [PERSON] et al. (2020) proposed a method based on U-Net architecture to fuse local and nonlocal features, upsampling by dilated convolution, and the corresponding spatial pyramid expanded receptive field and scale attention mechanism to identify the landslide caused by the earthquake in Jiuzhaigou, China [23]. [PERSON] et al. (2020) developed a CNN-based spatial channel attention mechanism to classify and identify landslides in Bijie City, China, from available satellite imagery and DEM datasets; this experiment concludes that the attention mechanism and DEM data can effectively improve the accuracy of landslide identification [24]. [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] et al. (2020) identified old loess landslides on Google Earth images using the two-stage algorithm Mask R-CNN; although the accuracy rate did not reach a high level, it confirmed the feasibility of Mask R-CNN to identify old landslides [26]. [PERSON] et al. (2021) proposed an improved U-Net neural network and completed the automatic identification of the deformation features of the landslide time series [27]. [PERSON] et al. (2021) used the Mask R-CNN model with ResNet101 as the backbone network and the transfer learning algorithm to complete landslide recognition in digital images of hilly areas obtained by drones, and the results show that the method is superior to the existing research in both algorithm performance and robustness [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) combined the object-based image analysis (OBIA) approach with the fully convolutional network (FCN) model to complete the landslide detection in Sentinel-2 images and verified the method's feasibility [30]. Therefore, deep learning methods have been applied to landslide identification. However, due to the diversity and complexity of landslides, these methods still have many problems to be solved. For example, in order to improve the ability to identify landslides, the model needs to learn a large number of data [31]; this is a key issue because there is very little landslide data currently available.