# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 1 Introduction
\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 Artificial Neural Network & A computational model for simulating the structure and function of biological neural networks & High class accuracy; strong learning ability & Requires a large number of parameters; unable to observe the learning process; the results are difficult to explain; long study time \\ \hline Support Vector Machine & Find the best hyperplane and separate data points of different categories with maximum spacing & Low generalization error rate; the computational cost is not significant; the results are easy to explain & Sensitive to parameter adjustment and kernel function selection; the original classifier, without modification, is only suitable for handling binary classification problems \\ \hline Random Forest & Generate multiple decision trees using random methods and train classification predictions on sample data & Can fully utilize limited samples; advantages of diversity and accuracy & Overfitting may occur on certain noisy issues \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._of low computational efficiency, high error rate and weak generalization ability. Therefore, it is very important to explore how to use the DeepLabv3+ deep learning model to better extract landslide edge information and improve the recognition accuracy in high-resolution remote sensing. In order to solve the above problems, this paper improves the DeepLabv3+ framework and proposes the DeepLabv3+ResNet101-ECA model. The fusion of multiple networks is the research trend in terms of deep learning. The proposed model integrates the advantages of the DeepLabv3+, ResNet101, and ECA modules and allows them to complement each other. The encoder uses ResNet101 as the backbone network to solve the problem of insufficient feature extraction ability, and the decoder integrates the ECA attention mechanism to effectively avoid the loss of key edge information. And as a combination model, it can effectively alleviate the problems of the low accuracy and poor generalization of a single model. Our contributions are as follows. We use satellite images combined with the DEM data from landslide datasets and use the real mask image as a tag ([[https://dx.doi.org/10.21227/ep6n-fm58](https://dx.doi.org/10.21227/ep6n-fm58)]([https://dx.doi.org/10.21227/ep6n-fm58](https://dx.doi.org/10.21227/ep6n-fm58))). Then, based on the DeepLabv3+ framework, a combined network model named DeepLabv3+-ResNet101-ECA is designed. The model uses ResNet101 to replace the backbone network to enhance the feature extraction ability of the model for small landslides, so as to solve the problem that the contours of small landslides cannot be captured. Incorporating a lightweight ECA attention mechanism into the model to focus on the most important regions in the landslide image can improve the retention of boundary information and enhance the landslide detection ability. Finally, the generalization ability of the model is verified by transfer learning. The DeepLabv3+-ResNet101-ECA model proposed in this paper uses deep learning technology to automatically extract complex terrain features and combines the attention mechanism to enhance the learning ability of key features, thereby improving the accuracy and robustness of landslide recognition. Due to the computational complexity of deep learning and its high dependence on data, DeepLabv3+-ResNet101-ECAye has potential limitations. The training and inference process of the model requires high computational resources, and optimization techniques such as pruning and quantization are required to reduce the computational cost. This model highly relies on high-quality, large-scale labeled datasets, which may limit the performance of the model if applied to regions where data are scarce or difficult to obtain.