Landslide detection using visualization techniques for deep convolutional neural network models

Hacıefendioğlu, Kemal; Demir, Gökhan; Başağa, Hasan Basri

doi:10.1007/s11069-021-04838-y

Cited by 35 publications

(15 citation statements)

References 40 publications

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“…The convolutional layer, central to CNN, consists of multiple convolutional kernels that linearly map the input data to extract finer feature information. The adoption of a shared weight strategy in the convolutional layer allows the entire network to be trained with fewer parameters compared to a fully connected network 34 . The pooling layer crucial in CNN, performs downsampling operations through various nonlinear functions to reduce feature size, retain essential details, and mitigate overfitting with different data 35 .…”

Section: Experimental Models and Methodsmentioning

confidence: 99%

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Yu,

Chen

2024

Sci Rep

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Landslides, recognized as a significant global natural disaster, necessitate an exploration of the impact of various resolution types in sampling strategies on Landslide Susceptibility Mapping (LSM) results. This study focuses on the segment from Zigui to Badong within the Three Gorges Reservoir Area, utilizing two resolution types: sampling resolution and spatial resolution, The Support Vector Machine (SVM) is employed to obtain LSM results, which are then analyzed using Receiver Operating Characteristic (ROC) curve, specific category accuracy and statistical methods. Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used to verify the reliability of the results. Additionally, five common machine learning models, including Logistic Regression (LR), are used to conduct experiments on four sampling resolutions (10 m,30 m,50 m and 70 m) to further investigate the effect of sampling resolution on LSM results. These are evaluated using a comprehensive quantitative method. The results reveal that increasing spatial resolution improves the prediction accuracy, while increasing sampling resolution produces a contrary effect. Furthermore, the impact of spatial resolution on LSM results is more pronounced than that of sampling resolution. Finally, Fanjiaping landslide and Huangtupo landslide are selected as references for comparative analysis, with the results aligning with engineering reality.

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Section: Experimental Models and Methodsmentioning

confidence: 99%

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Yu,

Chen

2024

Sci Rep

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“…The remote sensing images containing landslide areas were then input into a multiscale neural network to semantically segment the landslide areas, thereby accurately locating the landslide position and improving the accuracy of landslide detection. Hacıefendio glu et al [95] used the pre-trained ResNet50 model for the automatic identification of landslides, and the success rate was above 90%.…”

Section: Resnetmentioning

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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“…Second, based on the three convolutional neural networks ResNet-18, ResNet-50, and Vgg16, the accuracy of small-sample learning and large-sample learning is compared to determine the accuracy of the model. Finally, the monolinear and bilinear feature extraction effects of the three convolutional neural networks are compared through the CAM 32 diagram, and the results are explained.…”

Section: Experimental Analysismentioning

confidence: 99%

Image Recognition of Mine Water Inrush Based on Bilinear Convolutional Neural Network with Few-Shot Learning

Zhang,

Du,

Zhao

et al. 2024

ACS Omega

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With the increasingly widespread application of deep learning technology in the field of coal mines, the image recognition of mine water inrush has become a hot research topic. Underground environments are complex, and images have a high noise and low brightness. Additionally, mine water inrush is accidental, and few actual image samples are available. Therefore, this paper proposes an algorithm that recognizes mine water inrush images based on few-shot deep learning. According to the characteristics of images with coal wall water seepage, a bilinear neural network was used to extract the image features and enhance the network's fine-grained image recognition. First, features were extracted using a bilinear convolutional neural network. Second, the network was pre-trained based on cosine similarity. Finally, the network was fine-tuned for the predicted image. For single-line feature extraction, the method is compared with big data and few-shot learning. According to the experimental results, the recognition rate reaches 95.2% for few-shot learning based on a bilinear neural network, thus demonstrating its effectiveness.

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Landslide detection using visualization techniques for deep convolutional neural network models

Cited by 35 publications

References 40 publications

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

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

Image Recognition of Mine Water Inrush Based on Bilinear Convolutional Neural Network with Few-Shot Learning

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