Geological disaster monitoring technology along transmission line based on InSAR

Zhang, Jian; Li, Bocheng; Cai, Chuyu; Li, Xiaolong

doi:10.1109/ic2ecs57645.2022.10088150

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…This study investigates the escalating threat of geological disasters including landslides, mudslides, and ground subsidence, which result in profound human and economic losses [1][2][3][4][5][6]. The urgency of predicting and monitoring these hazards to mitigate their impact is underscored by a consistent rise in both the annual direct economic loss and the number of fatalities attributable to these geological calamities [7,8].…”

Section: Introductionmentioning

confidence: 99%

Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach

Ding¹,

Hu²

2023

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Geological disasters, characterized by their destructive nature, pose significant threats to both human life and ecological environments. The advent of remote sensing technology has rendered hyperspectral remote sensing images an integral data source in monitoring and predicting these phenomena. However, it is noted that minor variations and detailed nuances within the images are often overlooked by traditional computer vision and deep learning techniques. Furthermore, data imbalances during the training of deep learning models have been identified as a potential hindrance to optimal performance. Recognizing these issues and the inherent unpredictability of geological disasters, an innovative approach has been developed. This approach encapsulates an optical flow-based method for enhancing the edges of geological remote sensing images, an improved geological disaster monitoring model leveraging the Isolation Forest algorithm, and an efficient implementation strategy. The suggested methods present numerous advantages, including the acceleration of computations to augment real-time monitoring of geological disasters, an enhanced capacity for handling extensive data, an improved system stability and fault tolerance, and the preservation of fundamental strengths such as linear computational complexity, unsupervised learning, and non-parametric methodologies. By synthesizing these methodological improvements and advantages, a swift, efficient, and flexible strategy for enhancing the Isolation Forest model is put forth. This research supports the development of geological disaster monitoring and early warning systems grounded in computer vision and deep learning, presenting substantial technical aid for related tasks.

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Section: Introductionmentioning

confidence: 99%

Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach

Ding¹,

Hu²

2023

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Transmission Line Geological Hazard Detection Based on UAV LiDAR DEM and InSAR

Tang,

Fang,

Sun

et al. 2023

2023 International Conference on Smart Electrical Grid and Renewable Energy (SEGRE)

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Surface Subsidence Monitoring of Mining Areas in Hunan Province Based on Sentinel-1A and DS-InSAR

Zhang,

Gao,

Gan

et al. 2023

Sensors

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Monitoring the surface subsidence in mining areas is conducive to the prevention and control of geological disasters, and the prediction and early warning of accidents. Hunan Province is located in South China. The mineral resource reserves are abundant; however, large and medium-sized mines account for a low proportion of the total, and the concentration of mineral resource distribution is low, meaning that traditional mining monitoring struggles to meet the needs of large-scale monitoring of mining areas in the province. The advantages of Interferometric Synthetic Aperture Radar (InSAR) technology in large-scale deformation monitoring were applied to identify and monitor the surface subsidence of coal mining fields in Hunan Province based on a Sentinel-1A dataset of 86 images taken from 2018 to 2020, and the process of developing surface subsidence was inverted by selecting typical mining areas. The results show that there are 14 places of surface subsidence in the study area, and accidents have occurred in 2 mining areas. In addition, the railway passing through the mining area of Zhouyuan Mountain is affected by the surface subsidence, presenting a potential safety hazard.

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Geological disaster monitoring technology along transmission line based on InSAR

Cited by 3 publications

References 10 publications

Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach

Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach

Transmission Line Geological Hazard Detection Based on UAV LiDAR DEM and InSAR

Surface Subsidence Monitoring of Mining Areas in Hunan Province Based on Sentinel-1A and DS-InSAR

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