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

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 f...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tong Liu, Tao Chen, Ruiqing Niu, Antonio Plaza
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Deep neural network dense convolutional neural network (DenseNet) feature selection landslide detection Three Gorges Reservoir (TGR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/cfa6296c43114e60ace49a757d32d8f0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cfa6296c43114e60ace49a757d32d8f0
record_format	dspace
spelling	oai:doaj.org-article:cfa6296c43114e60ace49a757d32d8f02021-11-19T00:00:19ZLandslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China2151-153510.1109/JSTARS.2021.3117975https://doaj.org/article/cfa6296c43114e60ace49a757d32d8f02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9560068/https://doaj.org/toc/2151-1535Landslide 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.Tong LiuTao ChenRuiqing NiuAntonio PlazaIEEEarticleDeep neural networkdense convolutional neural network (DenseNet)feature selectionlandslide detectionThree Gorges Reservoir (TGR)Ocean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11417-11428 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep neural network dense convolutional neural network (DenseNet) feature selection landslide detection Three Gorges Reservoir (TGR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Deep neural network dense convolutional neural network (DenseNet) feature selection landslide detection Three Gorges Reservoir (TGR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Tong Liu Tao Chen Ruiqing Niu Antonio Plaza Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
description	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.
format	article
author	Tong Liu Tao Chen Ruiqing Niu Antonio Plaza
author_facet	Tong Liu Tao Chen Ruiqing Niu Antonio Plaza
author_sort	Tong Liu
title	Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
title_short	Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
title_full	Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
title_fullStr	Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
title_full_unstemmed	Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
title_sort	landslide detection mapping employing cnn, resnet, and densenet in the three gorges reservoir, china
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cfa6296c43114e60ace49a757d32d8f0
work_keys_str_mv	AT tongliu landslidedetectionmappingemployingcnnresnetanddensenetinthethreegorgesreservoirchina AT taochen landslidedetectionmappingemployingcnnresnetanddensenetinthethreegorgesreservoirchina AT ruiqingniu landslidedetectionmappingemployingcnnresnetanddensenetinthethreegorgesreservoirchina AT antonioplaza landslidedetectionmappingemployingcnnresnetanddensenetinthethreegorgesreservoirchina
_version_	1718420695847796736

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

Ejemplares similares