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• W3202069230 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 study, we constructed an accurate LDM model based on convolutional neural networks (CNNs), residual neural networks (ResNets), and dense convolutional neural networks (DenseNets) that considers 'ZY-3' high spatial resolution (HSR) data and conditioning factors (CFs). In this work, nineteen 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 (DNNs) 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 accuracy evaluation indexes, 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." @default.
• W3202069230 created "2021-10-11" @default.
• W3202069230 creator A5011327859 @default.
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• W3202069230 date "2021-01-01" @default.
• W3202069230 modified "2023-10-16" @default.
• W3202069230 title "Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China" @default.
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• W3202069230 cites W2012118327 @default.
• W3202069230 cites W2013713766 @default.
• W3202069230 cites W2076063813 @default.
• W3202069230 cites W2092694555 @default.
• W3202069230 cites W2097117768 @default.
• W3202069230 cites W2119843603 @default.
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• W3202069230 cites W2163039960 @default.
• W3202069230 cites W2171612326 @default.
• W3202069230 cites W2194775991 @default.
• W3202069230 cites W2278830514 @default.
• W3202069230 cites W2287960510 @default.
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• W3202069230 doi "https://doi.org/10.1109/jstars.2021.3117975" @default.
• W3202069230 hasPublicationYear "2021" @default.
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