# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 1 Introduction  
Landslides are slope gravity processes that reshape geological landforms by mobilizing landslide blocks [1]. The main reasons for their formation include the following three aspects: 1 geological reasons, such as material properties, structural characteristics, and permeability differences; 2 morphological reasons, such as topographic changes and natural processes; and 3 man-made causes, such as deforestation, artificial vibration, and soil erosion [2]. Landslides can cause huge losses to people's lives and property [3, 4, 5]. For example, the landslide in Xinmo Village, Diexi Town, MAO County, Sichuan Province, China, in 2017 left 83 people dead or missing, and the direct economic losses amounted to hundreds of millions of dollars [6]. Accurate and effective landslide recognition has always been a challenging problem. Landslide identification has been studied by many scholars over the years, and we arrange it in chronological order. In the early stage, landslide identification technology mainly relied on field investigation and the experience and judgment of geological experts [7]. [PERSON] et al. [8] determined the location, size and distribution of landslides caused by earthquakes in central Niigata Prefecture, Japan, by field investigation and using satellite or aerial image data. However, for some hard-to-reach or dangerous areas, there are certain difficulties in terms of the field survey, which may lead to incomplete data. [PERSON] et al. [9] used statistical analysis to evaluate the distribution of earthquake-induced landslides. However, the research process relied on ground measurements with high cost and limited coverage. [PERSON] et al. [10] conducted a field investigation of a large catastrophic landslide in Guanling County, Guizhou Province, which cost a lot of manpower, and the DAN3D model used showed certain deviations in relation to the prediction of the maximum depth. [PERSON] et al. [11] conducted a systematic comparative analysis of landslide cataloging maps generated through remote sensing image analysis and field investigation. The technology and experience of the researchers and the complexity of the study area had a certain impact on the analysis process. Due to the great changes in the acquisition methods and data sources in terms of landslide data, there are limitations when dealing with large-scale and multi-dimensional datasets, and the early landslide recognition methods cannot meet the recognition requirements. With the development of computer technology, machine learning has been widely introduced into the field of landslide detection. Machine learning methods are automated modeling techniques that analyze data to learn the underlying relationships and hidden patterns, thereby building analytical models. These methods are used to produce accurate and reproducible results for landslide susceptibility analysis through an iterative learning process. The commonly used machine learning methods mainly include logistic regression [12], decision trees [13], artificial neural networks [14], support vector machines [15], random forests [16], and so on. Table 1 lists the differences between these machine learning methods. [PERSON] et al. [17], based on the GIS data of Zhongxian County in the Three Gorges Reservoir area, used the logistic regression method to draw a detailed landslide susceptibility map and verified it. Logistic regression usually lacks classification accuracy, and the decision tree algorithm usually performs better than logistic regression, but it is prone to overfitting and has poor generalization ability [18; 19]. [PERSON] et al. [20] proposed a new method to establish a landslide displacement prediction model based on a random hidden weight neural network. However, the learning process of the artificial neural network is a black box, which makes the results difficult to interpret. The SVM algorithm solves the problem of the interpretability of artificial neural networks.