# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 6 Conclusions  
This study utilizes Google Earth images as the data source to establish an old landslide dataset for the Sichuan-Tibet river basin. Leveraging the YOLOv8 deep learning model, this paper replaces the Wise-IoU bounding box loss function and introduces the CBAM attention mechanism, constructing the YOLOv8-CW network that incorporates three different bounding box loss functions. A general detection method for old landslides is proposed, demonstrating strong detection capabilities for old landslides in the mountainous region of Southwest China and presenting promising application prospects. The main conclusions are as follows:  
1. In the detection of old landslides in the Sichuan-Tibet river basin, the improved YOLOv8-CW2 model achieves a detection accuracy of 80.1%, recall rate of 69.1%, mAP of 73.8%, and F1 score of 0.74. Compared to the original YOLOv8 model, the accuracy and recall rate are increased by 10.9% and 6%, respectively. The F1 score is increased from 0.66 to 0.74.
2. Comparing the detection results of the YOLOv8-CW2 model and the original YOLOv8 model on the old landslide dataset reveals substantial improvements in the accuracy of both model types. This indicates the effectiveness and feasibility of detecting old landslides in the Sichuan-Tibet river basin using the proposed optimization method.  
Figure 11: Heat map comparison ((**a1**–**a3**) indicates the actual boundary of the old landslide; (**b1**–**b3**) represents the YOLOv8; (**c1**–**c3**) represents the YOLOv8-SK; (**d1**–**d3**) represents the YOLOv8-CBAM; (**e1**–**e3**) represents the YOLOv8-SE.).  
3. To further enhance the stability and recognition ability of the model, multiple types of landslide data sources can be utilized for training, ultimately achieving accurate identification of multi-source data and various types of landslides. This approach provides timely and precise data support for landslide disaster rescue and disaster assessment.  
Despite achieving improvements in old landslide detection accuracy, this study acknowledges certain limitations. Due to the non-obvious characteristic form of old landslides, there are instances of missed detections, necessitating more precise classification and segmentation of old landslides. When detecting old landslides, it is recommended to combine InSAR and other applications, superimpose DEM and other multi-source data to construct comprehensive datasets, and utilize deep learning and other methods for accurate landslide detection. As geological disaster detection and identification enters the era of artificial intelligence, the use of automation technologies such as deep learning can significantly improve identification efficiency, holding significant implications for landslide prevention and mitigation across generations.  
Conceptualization, Q.Z.; data analysis, Y.L.; experimentation, Y.L.; funding support, M.D.; illustration painting, Z.L. and H.J.; image data collection, Y.L.; research design, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, M.D., W.H. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2021 YFC3000400), the National Natural Science Foundation of China (42374027), the Opening Fund of Key Laboratory of Smart Earth (KF2023 YB04-01), the Application and Demonstration of Comprehensive Governance and Scale Industrialization in the Sichuan-Tibet Region under the High-resolution Satellite Project (87-Y50G28-9001-22/23), Key R&D Program Projects in Zhejiang Province (2023C03177), and the Fundamental Research Funds for the Central Universities (300102262203).  
Not applicable.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
We are very grateful to Google Earth for the image data and platform support provided by ArcGIS, GMT, and Ultralytics.  
The authors declare no conflicts of interest.