Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Abstract

The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area.

A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images

“…Sreelakshmi [24] has developed an innovative deep-learning framework that uses visual saliency for automatic landslide identification, achieving 94% accuracy, surpassing existing models, and offering a promising tool for risk assessment and management in landslide-prone areas. Li et al [25] suggest integrating BotNet and ResNet feature maps to create an enhanced deeplabV3+ landslide identification technique. The modified YOLOv8 model can perform well on the validation set, according to experimental data, and it achieves excellent accuracy.…”