LandslideCL: towards robust landslide analysis guided by contrastive learning

Li, Penglei; Wang, Yi; Xu, Guosen; Wang, Lizhe

doi:10.1007/s10346-022-01981-w

Cited by 10 publications

(1 citation statement)

References 32 publications

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“…Traditional landslide boundary recognition methods mainly rely on manual visual interpretation, but they cannot meet the current needs of emergency rescue. In this context, by using remote sensing images, there is a relatively complete theoretical basis for applying computer intelligence technology to extract targets' boundary features [5]. However, the existing methods do not involve a deeper network structure, and it needs improve image recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Application of optimized CNN algorithm in landslide boundary detection

Wang,

Qiao

2023

J. meas. eng.

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Landslide, as a natural geological phenomenon with great harm, seriously threatens human social activities and life safety. It has a variety of latent and immeasurable destructiveness, which has a significant impact on the economic losses in rural areas. Therefore, it is urgent to take measures to accurately identify landslides to reduce their negative impacts. However, traditional manual visual interpretation has been unable to meet the current needs for emergency rescue of landslides, so computer intelligent methods have been paid attention to. This study proposes a new recognition network to address the problem of low accuracy of intelligent landslide boundary recognition methods. Firstly, the experiment incorporated boundary structure information into the Full Convolutional Network (FCN) for optimization, and constructed an Improved Full Convolutional Network (IFCN) model to better achieve image reconstruction. After that, Attention Mechanism (AM) is further introduced to achieve accurate detection of landslide boundaries in images, namely the IFCN-AM model. The attention mechanism introduced include spatial attention mechanism and multi-channel attention mechanism. Both are responsible for enhancing the language representation ability of the model and aggregating the interrelated features between different channels. The experimental results show that IFCN-AM has a 3 % to 7 % improvement in accuracy, recall, F1 value, and MIoU value.

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Section: Introductionmentioning

confidence: 99%