# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 4 Experiments and Results
### Transfer Learning Experiment  
Bijie and Luding are located in different geographical areas, and their terrain features are quite different. The Bijie area is mostly karst landform, and the terrain is complex and diverse; Luding is located on the edge of the Qinghai-Tibet Plateau, with higher terrain and mostly mountainous terrain. This difference in topography will result in landslides with different mechanisms, scales, and forms. In the case of large differences in terrain features, directly applying the pre-trained model to the new region is likely to lead to overfitting of the data features of the source domain. In order to solve the above problems, in the experiment, we normalize the data from the Bijie and Luding regions to reduce the inconsistency of numerical ranges caused by terrain differences, and we use data enhancement techniques (such as rotation, flipping, cropping, etc.) to enrich the target dataset and enhance the terrain features. A dropout layer and regularization are introduced in the construction of the neural network model to prevent the model from overfitting.  
In this paper, a parameter-based transfer learning method is used to train the landslide detection model. Firstly, the model is pre-trained on the Bijie dataset to obtain the pre-trained weights, and then the pre-trained weights are used for secondary training on the Luding landslide dataset. Although the objects in the two datasets are different, they share some underlying features, such as the edges and textures of the objects. This shared knowledge can be used by the model, so as to avoid the network learning from scratch, which can significantly improve the training speed and effect of the model.  
Table 8 records the model evaluation metrics before and after using transfer learning. The first model in the table is only trained and predicted on the Ludingdataset, while the second model is pre-trained on the Bijie dataset, and then it is trained and predicted on the Luding data. It can be seen from the table that the four evaluation indicators of the model after transfer learning are improved by 1.98%, 1.73%, 1.85% and 4.95%, respectively, and the model after transfer learning has better performance indicators.  
Figure 12 shows the predicted mask images of DeepLabv3+-ResNet101-ECA and DeepLabv3+-ResNet101-ECA-Trans-Learning. For landslide samples I, II, III, and IV, there is a certain degree of difference between the image edges predicted by DeepLabv3+-ResNet101-ECA without pre-training and the real mask edges. The images predicted by DeepLabv3+-ResNet101-ECA-Rrans-Learning have high agreement with the true mask. For the landslide sample V, the predicted output images of the two models are in good agreement with the reality. In summary, it is proved that transfer learning casues a good improvement in landslide recognition, and the model has good generalization ability.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **MIoU** \\ \hline DeepLabv3+-ResNet101-ECA & 0.9286 & 0.9254 & 0.9270 & 0.7743 \\ DeepLabv3+-ResNet101-ECA-Trans-Learning & 0.9484 & 0.9427 & 0.9455 & 0.8238 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation metrics for DeepLabv3+-ResNet101-ECA and DeepLabv3+-ResNet101-ECA-Trans-Learning. The DeepLabv3+-ResNet101-ECA representation model training and prediction are based on the Luding dataset. The DeepLabv3+–ResNet101-ECA-Trans-learning refers to pre-training on Bijie data and then prediction on Luding data.