Evolutionary analysis of slope direction deformation in the Gaojiawan landslide based on time-series InSAR and Kalman filtering DOI Creative Commons

Jingchuan Yao,

Runqing Zhan,

Jing-Sung Guo

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0316100 - e0316100

Published: Dec. 31, 2024

The existing landslide monitoring methods are unable to accurately reflect the true deformation of body, and use a single SAR satellite, affected by its revisit cycle, still suffers from limitation insufficient temporal resolution for monitoring. Therefore, this paper proposes method dynamic reconstruction evolutionary characteristic analysis Gaojiawan landslide’s along-slope based on ascending descending orbit time-series InSAR observations using Kalman filtering. Initially, employs gridded selection approach during processing, filtering coherent points standard deviation residual phases, thereby ensuring density quality extracted points. Subsequently, combination data converts line sight (LOS) into deformation. Finally, is utilized deformation, an characteristics conducted explore impact transportation infrastructure, significantly improving accuracy To verify feasibility algorithm, selects as typical study area. Based Sentinel-1 2016 2023, it extracts series slope body further internal infrastructure body. Experimental results show that has improved time six days. It was found two significant slips occurred in January June 2021, while other periods were relatively stable. Further discussion reveal there difference slip rate between upper lower parts shear stress caused dislocation

Language: Английский

Time-series InSAR landslide three-dimensional deformation prediction method considering meteorological time-delay effects DOI
Jichao Lv, Rui Zhang, Xin Bao

et al.

Engineering Geology, Journal Year: 2025, Volume and Issue: unknown, P. 107986 - 107986

Published: Feb. 1, 2025

Language: Английский

Citations

0
Landslide Identification from Post-Earthquake High-Resolution Remote Sensing Images Based on ResUNet–BFA DOI Creative Commons

Zhenyu Zhao,

Shucheng Tan, Yiquan Yang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 995 - 995

Published: March 12, 2025

The integration of deep learning and remote sensing for the rapid detection landslides from high-resolution imagery plays a crucial role in post-disaster emergency response. However, availability publicly accessible datasets specifically landslide remains limited, posing challenges researchers meeting task requirements. To address this issue, study develops releases dataset using Google Earth imagery, focusing on impact zones 2008 Wenchuan Ms8.0 earthquake, 2014 Ludian Ms6.5 2017 Jiuzhaigou Ms7.0 earthquake as research areas. contains 2727 samples with spatial resolution 1.06 m. enhance recognition, lightweight boundary-focused attention (BFA) mechanism designed Canny operator is adopted. This improves model’s ability to emphasize edge features integrated ResUNet model, forming ResUNet–BFA architecture identification. experimental results indicate that model outperforms widely used algorithms extracting boundaries details, resulting fewer misclassifications omissions. Additionally, compared conventional mechanisms, BFA achieves superior performance, producing recognition more closely align actual labels.

Language: Английский

Citations

0
Co-seismic landslide susceptibility mapping for the Luding earthquake area based on heterogeneous ensemble machine learning models DOI Creative Commons
Rui Zhang, Yunjie Yang, Tianyu Wang

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Oct. 1, 2024

Language: Английский

Citations

2
Landslide detection based on pixel-level contrastive learning for semi-supervised semantic segmentation in wide areas DOI
Jichao Lv, Rui Zhang, Renzhe Wu

et al.

Landslides, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

Language: Английский

Citations

1
Evolutionary analysis of slope direction deformation in the Gaojiawan landslide based on time-series InSAR and Kalman filtering DOI Creative Commons

Jingchuan Yao,

Runqing Zhan,

Jing-Sung Guo

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0316100 - e0316100

Published: Dec. 31, 2024

The existing landslide monitoring methods are unable to accurately reflect the true deformation of body, and use a single SAR satellite, affected by its revisit cycle, still suffers from limitation insufficient temporal resolution for monitoring. Therefore, this paper proposes method dynamic reconstruction evolutionary characteristic analysis Gaojiawan landslide’s along-slope based on ascending descending orbit time-series InSAR observations using Kalman filtering. Initially, employs gridded selection approach during processing, filtering coherent points standard deviation residual phases, thereby ensuring density quality extracted points. Subsequently, combination data converts line sight (LOS) into deformation. Finally, is utilized deformation, an characteristics conducted explore impact transportation infrastructure, significantly improving accuracy To verify feasibility algorithm, selects as typical study area. Based Sentinel-1 2016 2023, it extracts series slope body further internal infrastructure body. Experimental results show that has improved time six days. It was found two significant slips occurred in January June 2021, while other periods were relatively stable. Further discussion reveal there difference slip rate between upper lower parts shear stress caused dislocation

Language: Английский

Citations

0