An Overview of Landslide Detection: Deep Learning and Machine Learning Approaches

Zhang, Hong; Liu, Mingzhe; Wang, Tao; Jiang, Xin; Liu, Bingqi; Dai, Pengyu

doi:10.1109/icaibd51990.2021.9459015

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…Deep learning is a very powerful machine learning technique. It is an expansion of artificial neural networks (ANNs) but has a deeper, more complicated structure than ANNs [48] and has a wide range of applications in many fields. One of the most successful applications of deep learning methods, like convolutional neural networks (CNNs), is landslide recognition.…”

Section: Fundamentals Of Deep Learning To Recognize Landslidesmentioning

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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Section: Fundamentals Of Deep Learning To Recognize Landslidesmentioning

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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show abstract

“…With the breakthrough of artificial intelligence algorithms, deep learning technology has made substantial progress and become a research hotspot in various fields. Landslide recognition in remote sensing images can be divided into traditional machine learning algorithms and deep learning algorithms [7]. The machine learning algorithm only needs to input feature layers and training samples that can reflect the difference between landslides and surrounding ground objects.…”

Section: Artificial Intelligence Algorithm Landslide Extractionmentioning

confidence: 99%

“…𝑚 𝑖=1 (7) Where: y is the true value of pixels; 𝑦 ̂ is the predicted pixel value; m is the number of categories.…”

Section: Artificial Experimental Environmentmentioning

confidence: 99%

Remote sensing landslide recognition method based on LinkNet and attention mechanism

Yang

Luo

Wang

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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Rapid detection and identification of landslide areas are very important for disaster prevention and mitigation. Aiming at the problems of time-consuming and labor-intensive traditional landslide information extraction methods and low recognition efficiency, a remote sensing landslide recognition method based on LinkNet and convolution attention module was proposed. The model adopts the coding-decoding structure to improve the operation efficiency. The Convolutional Block Attention Module (CBAM) is applied to optimize the weight allocation from both channel and spatial dimensions to highlight the landslide feature information. And compared with the traditional U-Net and LinkNet models. The results show that the CBAM-LinkNet model has excellent performance in remote sensing landslide identification, which provides the possibility for rapid and accurate landslide identification.

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Research and Implementation of Landslide Labeling Method Oriented to Crowdsourcing Mode

Zhang,

Li,

Tong

et al. 2023

2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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An Overview of Landslide Detection: Deep Learning and Machine Learning Approaches

Cited by 5 publications

References 53 publications

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

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

Remote sensing landslide recognition method based on LinkNet and attention mechanism

Research and Implementation of Landslide Labeling Method Oriented to Crowdsourcing Mode

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