LSI-YOLOv8: An Improved Rapid and High Accuracy Landslide Identification Model Based on YOLOv8 From Remote Sensing Images

The rapid identification of landslides in mountainous terrain through remote sensing imagery after earthquakes or heavy rainfall is crucial for assessing the severity of the situation and providing timely assistance for post-disaster rescue operations. However, traditional manual interpretation methods and computer vision techniques tend to be inefficient and time-consuming. To address this problem, we propose a novel algorithm called LSI-YOLOv8, which builds upon the lightweight YOLOv8 to enable fast and accurate landslide identification using remote sensing imagery. In this study, we introduce optimization techniques integrated into the YOLOv8 model. Firstly, we incorporate an expandable residual attention Dilation-wise Residual Segmentation (DWRSeg) module to reconstruct its neck structure, reducing parameters and computational load. Additionally, we enhance the positioning ability of the model by replacing the bounding box loss function Complete Intersection over Union (CIoU) with Efficient Intersection over Union (EIoU), optimizing it to improve computing speed. Through comparative experiments with other state-of-the-art models, the accuracy of our LSI-YOLOv8 significantly outperforms Mask-RCNN, YOLOv5, YOLOv7, YOLOv8, and YOLOv9 model, with mAP@0.5 being 9.0%,7.7%, 4.0%, 6.3%, and 2.9% higher than other models, while mAP@0.5 :0.95 is 3.1%,3.6%, 2.7%, 4.8%, and 0.3% higher than others, respectively. Furthermore, the detecting speed of the LSI-YOLOv8 model is 73.2 f/s, which is notably faster by 15.5 f/s, 3.5 f/s, 5.1 f/s, 5.9 f/s, and 4.3 f/s respectively, compared to Mask-RCNN, YOLOv5, YOLOv7, YOLOv8, and YOLOv9. The proposed method provides valuable insights for advancing landslide identification methodologies. The focus of subsequent work will be more on multi-source data fusion, such as Digital Elevation Model (DEM) data and Synthetic Aperture Radar (SAR) data to remove the influence of some background information to improve the accuracy of landslide identification.