He Luhao, Zhou Yongzhang, Liu Lei, Zhang Yuqing, Ma Jianhua
Sun Yat-sen University Center for Earth Environment & Resources, Zhuhai, 519000, China.
School of Earth Science and Engineering, Zhuhai, 519000, China.
Sci Rep. 2025 Apr 11;15(1):12421. doi: 10.1038/s41598-025-95959-y.
In recent years, landslides have occurred frequently around the world, resulting in significant casualties and property damage. A notable example occurred in 2014, when a landslide in the Argo region of Afghanistan claimed over 2000 lives, becoming one of the most devastating landslide events in history. The increasing frequency and severity of landslides present significant challenges to geological disaster monitoring, making the development of efficient and accurate detection methods critical for disaster mitigation and prevention. This study proposes an intelligent recognition method for landslides, which is based on the latest deep learning model, YOLOv11-seg, which is designed to address the challenges posed by complex terrains and the diverse characteristics of landslides. Using the Bijie-Landslide dataset, the method optimizes the feature extraction and segmentation modules of YOLOv11-seg, enhancing both the accuracy of landslide boundary detection and the pixel-level segmentation of landslide areas. Compared with traditional methods, YOLOv11-seg performs better in detecting complex boundaries and handling occlusion, demonstrating superior detection accuracy and segmentation quality. During the preprocessing phase, various data augmentation techniques, including mirroring, rotation, and color adjustment, were employed, significantly improving the model's generalization performance and robustness across varying terrains, seasons, and lighting conditions. The experimental results indicate that the YOLOv11-seg model excels in several key performance metrics, such as precision, recall, F1 score, and mAP. Specifically, the F1 score reaches 0.8781 for boundary detection and 0.8114 for segmentation, whereas the mAP for bounding box (B) detection and mask (M) segmentation tasks outperforms traditional methods. These results highlight the high reliability and adaptability of YOLOv11-seg for landslide detection. This research provides new technological support for intelligent landslide monitoring and risk assessment, highlighting its potential in geological disaster monitoring.
近年来，世界各地山体滑坡频繁发生，造成了重大人员伤亡和财产损失。一个显著的例子发生在2014年，阿富汗阿尔戈地区的一次山体滑坡导致2000多人丧生，成为历史上最具破坏性的山体滑坡事件之一。山体滑坡频率和严重程度的增加给地质灾害监测带来了重大挑战，因此开发高效准确的检测方法对于减灾和预防灾害至关重要。本研究提出了一种基于最新深度学习模型YOLOv11-seg的山体滑坡智能识别方法，该模型旨在应对复杂地形和山体滑坡多样特征带来的挑战。该方法使用毕节山体滑坡数据集，对YOLOv11-seg的特征提取和分割模块进行了优化，提高了山体滑坡边界检测的准确性和滑坡区域的像素级分割效果。与传统方法相比，YOLOv11-seg在检测复杂边界和处理遮挡方面表现更好，具有更高的检测精度和分割质量。在预处理阶段，采用了包括镜像、旋转和颜色调整在内的各种数据增强技术，显著提高了模型在不同地形、季节和光照条件下的泛化性能和鲁棒性。实验结果表明，YOLOv11-seg模型在精度、召回率、F1分数和平均精度均值等几个关键性能指标上表现出色。具体而言，边界检测的F1分数达到0.8781，分割的F1分数达到0.8114，而边界框（B）检测和掩码（M）分割任务的平均精度均值优于传统方法。这些结果突出了YOLOv11-seg在山体滑坡检测中的高可靠性和适应性。本研究为山体滑坡智能监测和风险评估提供了新的技术支持，凸显了其在地质灾害监测中的潜力。
