Recognition Method of Landslide Remote Sensing Image based on EfficientNet

Chai, Jingwen; Nan, Yutong; Guo, Rong; Lin, Yuqiu; Liu, Yutong

doi:10.1109/icetci55101.2022.9832276

Cited by 2 publications

(1 citation statement)

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“…It plays an increasingly important role in disaster prevention and mitigation applications. After reviewing the currently available literature on landslide recognition in optical remote sensing images, we found that several classic models such as ResNet [24,25], YOLO [26][27][28][29][30], Mask R-CNN [31][32][33][34], U-Net [28,[35][36][37], DeeplabV3+ [38][39][40], Transformer [41][42][43], and EfficientNet [44,45] and several open landslide datasets such as Bijie landslide dataset [24], HR-GLDD dataset [46], CAS Landslide Dataset [47], and so on, have been popularly used for landslide recognition. In this paper, we will first introduce the fundamentals of landslide recognition based on deep learning and then discuss and analyze the current development status of each type of model; finally, we will compare the advantages and disadvantages of each model and analyze the development trends of landslide identification.…”

Section: Introductionmentioning

confidence: 99%

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Cheng,

Wang,

Huang

et al. 2024

Remote Sensing

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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.

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