Abstract:

An open-pit mine landslide identification method was proposed based on object-oriented annotation datasets and the Res-U-Net model to realize accurate identification and early warning of open-pit mile landslide disasters. Firstly, the mine landslide image data in the study area were obtained by UAV aerial survey. Secondly, the multi-scale-spectral segmentation method and threshold separation principle were applied to divide and classify the open-pit mine landslide data, and the landslide dataset was developed based on the object-oriented method. Then, the U-Net network was used as the infrastructure to propose a landslide identification semantic segmentation model based on Res-U-Net by integrating the residual module into each convolutional layer. Finally, the datasets constructed by different methods were used to identify landslides, and the Res-U-Net model was compared with the widely used semantic segmentation models, Fully Convolutional Networks (FCN), and U-net. The results indicated that the landslide data set based on object-oriented annotation had better landslide identification performance when compared to the traditional manual annotation dataset, resulting in improvements in identification accuracy, recall rate, F₁ score, and kappa coefficient of more than 12%. The landslide identification accuracy of the Res-U-Net model was more than 0.8, realizing the accurate landslide open-pit mine disaster identification.

Key words: unmanned aerial vehicle image, deep learning, landslide disaster, intelligent identification, object oriented, Res-U-Net