Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images
点击次数：
影响因子：
4.2
所属单位：
中南大学地球科学与信息物理学院
教研室：
GIS
发表刊物：
Remote Sensing
刊物所在地：
国外
关键字：
landslide; identification; deep learning; model; remote sensing image
摘要：
Against the backdrop of global warming and increased rainfall, the hazards and potential
risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance
for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide
identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy.
This article first provides an overview of deep learning technology and its basic principles, as
well as the current status of landslide remote sensing databases. Then, classic landslide deep learning
recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet,
DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were
extensively analyzed. Finally, the current constraints of deep learning in landslide identification were
summarized, and the development direction of deep learning in landslide identification was analyzed.
The purpose of this article is to promote the in-depth development of landslide identification research
in order to provide academic references for the prevention and mitigation of landslide disasters
and post-disaster rescue work. The research results indicate that deep learning methods have the
characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention
should be paid to the development of emerging deep learning models in landslide recognition in
the future.
合写作者：
Cheng Huang, Zixuan Wang
第一作者：
Gong Cheng
论文类型：
期刊论文
通讯作者：
Yingdong Yang
论文编号：
1787
学科门类：
工学
一级学科：
地质资源与地质工程
文献类型：
J
卷号：
16
期号：
10
是否译文：
否
收录刊物：
SCI
附件：
学位：博士学位
学科：地质资源与地质工程. 地质学. 测绘科学与技术
所在单位：地球科学与信息物理学院