# Automatic Identification of Landslides Based on Deep Learning
###### Abstract  
A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification. deep learning; semantic segmentation; PSPNet; landslide +
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Pixel-based landslide identification methods focus on pixel values and pixel value changes in remote sensing images and classify them according to pixel changes or change characteristics [7]. Although this method overcomes the shortcomings of visual interpretation, it is only judged by a single pixel value; moreover, it does not consider the correlation between pixels, resulting in an indistinct recognition of landslide edge regions and poor performance [8]. Object-oriented landslide identification methods utilize the attribute features of raw data (such as texture, spectrum, etc.) to classify remote sensing images by one or more attributes [9]. However, this method uses one or more attribute features to set thresholds, and the process is complicated and cannot process large-scale remote sensing images in time. With the continuous increase in high-resolution remote sensing images, how to quickly and efficiently identify targets from massive remote sensing images has become a key problem for scholars and experts to study. In recent years, with the rapid development of deep learning, computer vision and image processing technology have been introduced into the field of remote sensing as a new method for remote sensing image classification and target detection [10]. Research has shown that deep learning methods do not require many of the data provided in traditional landslide identification methods to help identify landslides; they only need enough landslide images as samples and training, which greatly simplifies the complex calculation process, makes up for the shortcomings of the above-mentioned traditional landslide identification methods, and realizes automatic identification of landslides. At the same time, deep learning methods are higher accuracy than the traditional landslide identification methods [11]. The main contributions of this article are as follows:  
1. We process the landslide data in the Bijie landslide dataset, create a landslide dataset, and preprocess the dataset (data cleaning, data enhancement);
2. On the landslide dataset, we use three models (U-Net, DeepLab v3+ and PSPNet) to conduct experiments and test the performance changes in the models when different classification networks are used as the backbone network;
3.