Automatic Identification of Thaw Slumps Based on Neural Network Methods and Thaw Slumping Susceptibility

Huarui Zhang; Huini Wang; Jun Zhang; Jing Luo; Guoan Yin

doi:10.1007/s13753-023-00504-y

Volume 14 Issue 4

Sep. 2023

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Article Navigation > International Journal of Disaster Risk Science > 2023 > 14(4): 539-548

Huarui Zhang, Huini Wang, Jun Zhang, Jing Luo, Guoan Yin. Automatic Identification of Thaw Slumps Based on Neural Network Methods and Thaw Slumping Susceptibility[J]. International Journal of Disaster Risk Science, 2023, 14(4): 539-548. doi: 10.1007/s13753-023-00504-y

Citation:

Huarui Zhang, Huini Wang, Jun Zhang, Jing Luo, Guoan Yin. Automatic Identification of Thaw Slumps Based on Neural Network Methods and Thaw Slumping Susceptibility[J]. International Journal of Disaster Risk Science, 2023, 14(4): 539-548. doi: 10.1007/s13753-023-00504-y

Citation:

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Automatic Identification of Thaw Slumps Based on Neural Network Methods and Thaw Slumping Susceptibility

doi: 10.1007/s13753-023-00504-y

Huarui Zhang¹,
Huini Wang^{1
,
,},
Jun Zhang¹,
Jing Luo^{2
,
,},
Guoan Yin²

1. School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, 430074, China;
2. State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China

Funds:

This study was jointly supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK0905), the National Science Foundation of China (Grant No. 42071097), the foundation of the State Key Laboratory of Frozen Soil Engineering (Grant No. SKLFSE202003), and the 14th Graduate Education Innovation Fund of Wuhan Institute of Technology (Grant No. CX2022164).

Accepted Date: 2023-08-15
Publish Date: 2023-08-25

Abstract

Abstract

Thaw slumping is a periglacial process that occurs on slopes in cold environments, where the ground becomes unstable and the surface slides downhill due to saturation with water during thawing. In this study, GaoFen-1 remote sensing and fused multi-source feature data were used to automatically map thaw slumping landforms in the Beilu River Basin of the Qinghai–Tibet Plateau. The bi-directional cascade network structure was used to extract edges at different scales, where an individual layer was supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all convolutional neural network outputs. Additionally, we conducted a 5-year multi-scale feature analysis of small baseline subset interferometric synthetic aperture radar deformation, normalized difference vegetation index, and slope, among other features. Our study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfigured neural networks, ResNet101, VGG16, and ResNet152. Through verification using site surveys and multi-data fusion results, we obtained the best ResNet101 model score of intersection over union of 0.85 (overall accuracy of 84.59%).The value of intersection over union of the VGG and ResNet152 are 0.569 and 0.773, respectively. This work provides a new insight for the potential feasibility of applying the designed edge detection method to map diverse thaw slumping landforms in larger areas with high-resolution images.
- Bi-directional cascade network,
- Remote sensing,
- SBAS-InSAR,
- Thaw slumping

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References(24)

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