# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 5 Conclusions  
In order to improve the accuracy and generalization of landslide recognition, this paper proposes a DeepLabv3+-ResNet101-ECA model, which uses ResNet101 as the backbone network and integrates the ECA attention mechanism into the model to improve the performance of landslide recognition.  
In this paper, DEM is used as a feature of the image and the original RGB image is fused and input into the neural network, and the landslide features can be extracted from multiple angles. The experimental results are compared and analyzed with the mask as a label and the output mask.  
Compared with the DeepLabv3+ model, the proposed model has the precision, recall, F1 score and MIoU increased by 1.17%, 2%, 0.96% and 2.36%, respectively. The improvement in these metrics is large. An increase in precision means that the overall prediction of the model is more correct and there are fewer misclassifications; The improved recall shows that the model is more effective in identifying all the real landslide events. The improvement in the F1 score reflects that the model achieves a better balance between accuracy and recall, and it improves the overall performance of the model. The improvement of the MIoU indicates that the model is more accurate in identifying the boundary of the landslide area. The improvement of these indicators not only improves the theoretical performance of the model but also provides disaster management departments with more accurate landslide disaster areas, which is helpful to quickly deploy rescue forces and resources and reduce disaster losses.  
In addition, transfer learning is used to apply the pre-trained model to the Luding region, which verifies the generalization ability of DeepLabv3+-ResNet101-ECA. The results show that our designed model improves the accuracy of landslide detection to a certain extent and provides timely and accurate technical support for landslide identification evaluation and disaster prevention and mitigation decision-making.  
The model proposed in this study has the ability to be integrated into the automated disaster management system. By integrating it into the existing disaster warning platform, the model can provide a continuous and automatic landslide warning service, which can monitor and assess the potential landslide risk in real time, thus greatly improving the disaster response efficiency and reducing casualties and property losses. This is not only crucial for the rapid response of landslide hazards but also provides a reference for other types of geological hazard monitoring, which has a wide range of application prospects and promotion value.  
Despite the progress made in landslide detection accuracy, there is some room for improvement in this research. More accurate classification and segmentation of landslides is needed due to the inconspicuous shape of landslide features and omissions. In future work, we will fuse multi-source remote sensing data to obtain richer surface information, combine relevant terrain information, and fuse additional landslide texture features for landslide recognition.  
Conceptualization, X.C. and S.W.; methodology, X.C. and S.W.; writing--original draft preparation, S.W., M.S. and D.W.; writing--review and editing, S.W., V.D. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science Research Project of Hebei Education Department under Grant ZC2023108, in part by the Langfang Science Technology Research Self-Financing Project under Grant 2024011018, and in part by the China Scholarship Council (CSC).  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the editor and the reviewers for their helpful comments.  
The authors declare no conflicts of interest.