# Improving Landslide Recognition on UAV Data through Transfer Learning
## 1 Introduction  
China is a country with frequent geological disasters, causing serious casualties and economic losses every year. The statistics showed that the direct economic loss caused by geological disasters in China is 50.2 billion yuan in 2020. In order to protect the safety of people's lives and property, the management of all emergency processes coming into effect after the disaster occurs is crucial to the disaster relief work. Satellite remote sensing has the advantage of obtaining information in a large area. However, the timeliness of obtaining image data is subject to the satellite orbit, which cannot ensure that the high-quality remote sensing images are available in the disaster area in a short time [1]. In recent years, UAV technology has been developing rapidly. The advantages of the UAV system are mainly related to fast data collection, short period taken, high safety and all-day observation, and it has been widely used in the disaster monitoring [2]. UAV can obtain disaster images when it is difficult for the rescue team to reach situations on the ground, which provides an important technical means to fully grasp the disaster situation. Whether it is by use of remote sensing images or UAV images, a traditional expert visual interpretation is the mainstream method for post-disaster assessment and reconstruction [3, 4]. However, the method has inefficiency and high expert resource costs. The interpretation accuracy may be poor [5, 6, 7] and may delay the emergency rescue. Therefore, new technologies and methods must be adopted to automatically identify landslide disasters [8, 9, 10]. Automated extraction methods such as the object-oriented and machine learning-based image classification, in recent years, have been used alongside support vector machines (SVM), random forests (RF), artificial neural networks (ANNs), convolutional neural networks (CNN), deep learning and other methods for landslide detection [11, 12, 13, 14]. [PERSON] et al. [15] took landslides near the Panda Sea caused by the Jiuzhaigou earthquake as the research area and introduced the object-oriented analysis method to realize landslide extraction. Based on the object-oriented image analysis, [PERSON] et al. [16] extracted landslide disaster information using a random forest classification algorithm. [PERSON] et al. [17] used the Bayesian statistical framework to semi-automatically realize landslide information extraction and classification. In recent years, deep learning technology, especially the deep convolutional neural network, has attracted the attention of many scholars [18; 19]. Deep learning (DL) processes complex images and extracts features from the original images very effectively [20; 21]. There are two typical types of deep learning approaches for image recognition: the first are region-based methods, such as RCNN, fast-cnn, fast-cnn and r-fcn [22; 23; 24; 25]. The second type is typified by the regression-based methods, such as YOLO and SSD algorithms [26; 27]. The second type of method is faster than the first type, but with lower accuracy because it generates bounding boxes in a single network. These methods have become the mainstream deep learning approaches for the extraction of the information from remote sensing images [28]. Deep learning methods can automatically detect landslides in a large area, which can better meet the emergency response requirements. [PERSON] et al. [29] and [PERSON] et al. [30] extracted landslide information from remote sensing images using a depth convolution neural network. The combination of artificial intelligence technology and remote sensing monitoring is expected to improve the automation level of landslide extraction and provide first-hand information for post-disaster emergency rescue [31]. However, target detection based on deep learning often requires massive amounts of training data [32; 33]. Few public datasets are open for landslide disasters. The data we acquired in the study area was not enough to train a model with good adaptability.