Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Abstract

Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this article, we constructed an accurate LDM model based on convolutional neural networks, residual neural networks, and dense convolutional neural networks (DenseNets) that considers “ZY-3” high spatial resolution (HSR) data and conditioning factors (CFs). In this article, 19 factors based on remote sensing (RS) images, topographical and geological data associated with historical landslide locations were randomly divided into training (70% of total) and testing (30%) datasets. The experimental results show that the accuracy (ACC) of these three LDM models is above 0.95, indicating that the deep neural networks aimed at landslide detection performed well. Furthermore, DenseNet with RS images and CFs can accurately detect landslides. Specifically, DenseNet with RS images and CFs outperforms the other five models by considering the evaluation metrics, which exhibited Kappa coefficient improvements of 0.01–0.04 and ACC improvements of 0.02–0.3%. Among all the factors, elevation factor has a high importance of 0.727, which is the most important factors found in this landslide model construction experiment.

Keywords

• Deep neural network
• dense convolutional neural network (DenseNet)
• feature selection
• landslide detection
• Three Gorges Reservoir (TGR)