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,F,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.
维普
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