Impact Factor:4.2
Affiliation of Author(s):中南大学地球科学与信息物理学院
Teaching and Research Group:GIS
Journal:Remote Sensing
Place of Publication:国外
Key Words:landslide; identification; deep learning; model; remote sensing image
Abstract: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.
Co-author:Cheng Huang, Zixuan Wang
First Author:Gong Cheng
Indexed by:Journal paper
Correspondence Author:Yingdong Yang
Document Code:1787
Discipline:Engineering
First-Level Discipline:Geological Resources and Geological Engineering
Document Type:J
Volume:16
Issue:10
Translation or Not:no
Included Journals:SCI