Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

Zhang, Pengfei; Xu, Chong; Ma, Siyuan; Shao, Xiaowei; Tian, Yingying; Wen, Boyu

doi:10.3390/rs12233992

Cited by 47 publications

(24 citation statements)

References 37 publications

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“…Landslide inventory mapping is the key to emergency rescue and landslide disaster loss assessment [68]. Meanwhile, improving the efficiency of spatial prediction is also important for technical experts to obtain detailed landslide disaster distribution.…”

Section: Discussionmentioning

confidence: 99%

Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslides have caused tremendous damage to human lives and property safety. However, the complex environment of mountain landslides and the vegetation coverage around landslides make it difficult to identify landslides quickly and efficiently using high-resolution images. To address this challenge, this article presents a feature-based constraint deep U-Net (FCDU-Net) method to detect rainfall-induced mountainous landslides. Usually, the vegetation in the landslide area is severely damaged, and the vegetation coverage can indirectly reflect the spatial extent of the landslide. Meanwhile, the texture features of high-resolution images can characterize the surface environment of landslide hazards to a certain extent. We first introduce auxiliary features of normalized difference vegetation index and gray-level co-occurrence matrix into the proposed method to further improve the detection performance. Then, to minimize the information redundancy of these features and the image, we combine Relief-F and Deep U-Net to screen the optimal features to effectively identify accurate and detailed landslide boundaries. Compared with traditional semantic segmentation methods, the FCDU-Net method can capture fine-grained details in high-resolution images and produce more accurate segmentation results. We conducted experiments by applying the proposed method and other most popular semantic segmentation methods to a high-resolution RapidEye image in Rio de Janeiro, Brazil. The results demonstrate that the FCDU-Net Manuscript

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Section: Discussionmentioning

confidence: 99%

Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To verify the practicability of RS-UNet in landslide mapping, the landslides caused by earthquakes in Hokkaido, Japan, were selected [41] for landslide mapping tests. The basic situation of the landslide is shown in Figure 13; landslides occurred mainly in vegetation-covered mountains; in addition, farmland and residential areas were present in the background.…”

Section: E Application In Landslide Mappingmentioning

confidence: 99%

Reg-SA–UNet++: A Lightweight Landslide Detection Network Based on Single-Temporal Images Captured Postlandslide

Niu

Gao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslide detection based on remote sensing images is an effective method for rapidly and accurately detecting landslide regions, which can aid in disaster prevention and mitigation. Landslide detection methods based on semantic segmentation can be used to delineate the scope of landslides while detecting their location. Most existing models use multi-temporal or geological data to improve accuracy. However, the large amount of data introduces additional parameters, consuming significant computing resources. Therefore, this study proposes Reg-SA-UNet++, a model for landslide detection, which uses a single-temporal image captured post-landslide. Reg-SA-UNet++ is based on UNet++ with the following modifications: deep supervised pruning is removed for fewer model parameters and increased detection accuracy; RegNet is employed to replace the convolutional blocks in the encoding process to reduce the number of parameters and improve feature acquisition and attention modules are added at the connection of the convolutional blocks of each layer to strengthen the model's attention to landslide features. The overall accuracy and F1 score of the Reg-SA-UNet++ model for the constructed landslide dataset (93.37% and 92.41%, respectively) and landslide mapping (97.09% and 96.10%, respectively) verify the effectiveness of the proposed model in detecting landslides from remote sensing images.

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“…Vegetation coverage refers to the variation ratio of the vertical projection area of all plants including crops, shrubs, trees and weeds, branches, and leaves on the ground of their growth area to the area of the study statistical area [24,25]. During the processing of the data set, standardized cropping has been carried out to ensure the same size of the background area.…”

Section: Morphological Feature Extraction Accuracy and Growthmentioning

confidence: 99%

Measurement Method of Plant Phenotypic Parameters Based on Image Deep Learning

Dong

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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This article applies deep learning and electromechanical technology to plant phenotype measurement. First, an electromechanical device is designed to collect plant phenotype images, which solves the difficulty of collecting deep learning training data. The data set required for deep learning model training for plant phenotype detection is made by an automated method. This paper takes the Lactuca sativa plant image as an example and uses the ASM-based data enhancement method to solve the problem of insufficient image data of Lactuca sativa leaf pests and effectively avoid the phenomenon of overfitting. The plant image recognition method based on deep learning proposed breaks through the limitations of plant local feature recognition, gets rid of the limitation of highly specialized data collection, lowers the threshold of plant image recognition, and has advantages in recognition speed and accuracy. This method requires a large amount of training data. In the future, we can explore the collection of massive plant pictures from the Internet as a training set to achieve rapid iteration and optimization of the model.

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Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

Cited by 47 publications

References 37 publications

Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil

Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil

Reg-SA–UNet++: A Lightweight Landslide Detection Network Based on Single-Temporal Images Captured Postlandslide

Measurement Method of Plant Phenotypic Parameters Based on Image Deep Learning

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