# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 1 Introduction
[PERSON] [21] proposed to use CUDA and OpenMP to accelerate the SVM classification and reduce the amount of computation. [PERSON]. [PERSON] et al. [22] integrated the condition factors into SVM to evaluate the correlation between landslide occurrence and each condition factor. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Main Principles** & **Advantages** & **Disadvantages** \\ \hline Logistic Regression & Establish a regression formula based on existing data to classify the boundary line & The principle is simple; low computational cost & Easy to underfit; classification accuracy may not be high \\ \hline Decision Tree & Building a decision model using a tree-like structure based on data attributes & This algorithm has a certain degree of robustness; it can handle irrelevant feature data & Possible overfitting issues may arise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Principles, advantages, and disadvantages of different machine learning methods. In recent years, deep learning has achieved good performance in the field of image processing [23; 24; 25; 26]. In the process of image recognition, deep learning network models do not need to manually design the feature extraction process and are able to automatically learn complex feature representations from data. Specifically, traditional methods rely on manually designed feature extractors, which usually require domain expertise and are difficult to adapt to a changing data distribution. In contrast, deep learning automatically learns multi-level features through multi-layer neural networks, which greatly improves the generalization ability of the model and the ability to capture complex patterns [27; 28]. The application of deep learning in the field of landslide recognition has become the research focus of many scholars. [PERSON] [29] proposed a landslide warning algorithm for the K-meansResNet model based on the ResNet network to realize landslide warning. [PERSON] [30] proposed an integrated method for landslide recognition in remote sensing images by combining a deep CNN and change detection. [PERSON] et al. [31] classified the causes of natural landslides and engineering landslides, established a seven-layer improved CNN, and accurately predicted the probability of landslide susceptibility, which reflects the probability of future landslides in a particular area, is accurately predicted, and is quantified by a numerical value between zero and one, where higher values indicate a higher risk of landslides in the area. The DeepLab model has been widely used in various image analysis tasks, including landslide recognition, due to its excellent semantic segmentation ability. [PERSON] et al. [32] proposed a semantic segmentation method for remote sensing images based on the DeepLabv3 network, which improves the segmentation accuracy by adding the dilation factor in the spatial pyramid pooling (ASPP) parallel structure. [PERSON] et al. [33] proposed a CNN-based landslide segmentation method to identify landslides using the DeepLabv3+ model. [PERSON] et al. [34] proposed a landslide image semantic segmentation method (CLBP-DeepLabv3+) combining DeepLabv3+ and the complete local binary pattern (CLBP). However, there are still some challenges in terms of model performance when the DeepLabv3+ framework is applied to the field of landslide recognition. Firstly, the encoder part of DeepLabv3+ usually uses a pre-trained deep convolutional neural network (e.g., MobileNet) as the backbone network. Its feature extraction capability may not be fully adapted to the specific task of landslide recognition, resulting in insufficient landslide feature learning. Secondly, the pooling layer and multiple down-sampling operations in the decoder of the DeepLabv3+ framework will inevitably lose image details, especially fine structures such as landslide boundaries. In addition, a single model has the problems