Post Evaluation of Slope Cutting on Loess Slopes under Long-Term Rainfall Based on a Model Test

Liu, Guodong; Zhou, Zhijun; Xu, Shiqiang; Cheng, Yuanmeng

doi:10.3390/su142315838

Cited by 1 publication

(1 citation statement)

References 53 publications

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“…Therefore, landslide detection and modeling plays a crucial role in their analysis [4]. These studies are categorized based on the triggering factors of landslides [5], primarily including those induced by rainfall [6][7][8] and earthquakes [9][10][11]. Key research topics in landslides include the prediction of displacement in single landslide [9,12], identification of landslides [13][14][15], susceptibility assessment [2,16], hazard evaluation [17], and risk assessment [18,19].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Landslide Detection with SBConv-Optimized U-Net Architecture Based on Multisource Remote Sensing Data

Song,

Zou,

et al. 2024

Land

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This study introduces a novel approach to landslide detection by incorporating the Spatial and Band Refinement Convolution (SBConv) module into the U-Net architecture, to extract features more efficiently. The original U-Net architecture employs convolutional layers for feature extraction, during which it may capture some redundant or less relevant features. Although this approach aids in building rich feature representations, it can also lead to an increased consumption of computational resources. To tackle this challenge, we propose the SBConv module, an efficient convolutional unit designed to reduce redundant computing and enhance representative feature learning. SBConv consists of two key components: the Spatial Refined Unit (SRU) and the Band Refined Unit (BRU). The SRU adopts a separate-and-reconstruct approach to mitigate spatial redundancy, while the BRU employs a split-transform-and-fuse strategy to decrease band redundancy. Empirical evaluation reveals that models equipped with SBConv not only show a reduction in redundant features but also achieve significant improvements in performance metrics. Notably, SBConv-embedded models demonstrate a marked increase in Recall and F1 Score, outperforming the standard U-Net model. For instance, the SBConvU-Net variant achieves a Recall of 75.74% and an F1 Score of 73.89%, while the SBConvResU-Net records a Recall of 70.98% and an F1 Score of 73.78%, compared to the standard U-Net’s Recall of 60.59% and F1 Score of 70.91%, and the ResU-Net’s Recall of 54.75% and F1 Score of 66.86%. These enhancements in detection accuracy underscore the efficacy of the SBConv module in refining the capabilities of U-Net architectures for landslide detection of multisource remote sensing data. This research contributes to the field of landslide detection based on remote sensing technology, providing a more effective and efficient solution. It highlights the potential of the improved U-Net architecture in environmental monitoring and also provides assistance in disaster prevention and mitigation efforts.

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