scholarly journals Debris-flows scale predictions based on basin spatial parameters calculated from Remote Sensing images in Wenchuan earthquake area

Author(s):  
Huaizhen Zhang ◽  
Tianhe Chi ◽  
Jianrong Fan ◽  
Tianyue Liu ◽  
Wei Wang ◽  
...  
Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5191
Author(s):  
Chang Li ◽  
Bangjin Yi ◽  
Peng Gao ◽  
Hui Li ◽  
Jixing Sun ◽  
...  

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.


Geomorphology ◽  
2016 ◽  
Vol 253 ◽  
pp. 468-477 ◽  
Author(s):  
W. Hu ◽  
X.J. Dong ◽  
Q. Xu ◽  
G.H. Wang ◽  
T.W.J. van Asch ◽  
...  

2017 ◽  
Vol 76 (17) ◽  
Author(s):  
Xiaojun Guo ◽  
Peng Cui ◽  
Lorenzo Marchi ◽  
Yonggang Ge

2013 ◽  
Vol 70 (2) ◽  
pp. 1417-1435 ◽  
Author(s):  
Wei Zhou ◽  
Chuan Tang ◽  
Th. W. J. Van Asch ◽  
Chunhua Zhou

2010 ◽  
Vol 7 (3) ◽  
pp. 226-233 ◽  
Author(s):  
Yanfu Li ◽  
Zhaoyin Wang ◽  
Wenjing Shi ◽  
Xuzhao Wang

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