Design of GIS-based Monitoring and Early-warning System of Landslide Hazard in Diao Zhongba

Li, Xue Ping; Li, Yun An

doi:10.1016/j.egypro.2012.01.187

Cited by 6 publications

(2 citation statements)

References 1 publication

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“…Landslides are characterized by their large scale, suddenness, and concealment. In recent years, the frequency of landslides has been increasing year by year due to the extension of the scope of human activities to the natural environment (Li and Li, 2012). Therefore, landslides need to be identified using widescale landslide identification techniques.…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Fusion Network to Multi Source Heterogeneous Data for Landslide Recognition

Han²,

Li³

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, the frequency of landslide disasters has been increasing year by year due to the extension of human activities to the natural environment. Fast and detailed landslide surveys are important for landslide disaster prediction and management. There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of fused data for prediction. Therefore the upper limit of accuracy they can achieve is low. This paper proposes a landslide identification network model based on the spatio-temporal fusion of heterogeneous data from multiple sources. The model takes observations such as time-series optical remote sensing images, DEM, geological formations, and meteorological data as inputs. To address the problems of non-uniform data forms and redundancy caused by time-series data, we design the temporal phase fusion module of coupled CNN-LSTM to fuse the temporal features of multi-source data based on the extraction of their spatial features. Subsequently, we design the spatial feature fusion module based on DCNN-DBN to realize the deep expression of temporal phase and spatial features of landslides and improve the recognition efficiency and accuracy of the network. Through experimental verification, the AUC value of our proposed model is 0.8976, the F1 score is 0.8352, and the MIoU is 0.8624. The evaluation results reflect that the model can provide support for large-scale landslide disaster investigation.

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Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Fusion Network to Multi Source Heterogeneous Data for Landslide Recognition

Han²,

Li³

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Its core concept lies in collecting effective and reliable environmental information, increasing utilization rates, sharing a degree of environmental information by advanced information technology, and providing timely and scientific foundations for environmental monitoring and management. Li and Li (2012) designed a monitoring and warning system for facilitating the reduction of capital investment and preventing geological disasters. The automation and information processing for landslide hazards in this system can provide a basis for early warning of landslides in a village called Diao Zhongba.…”

Section: D Data Visualizationmentioning

confidence: 99%

A BIM-based monitoring system for urban deep excavation projects

Hsiung

2015

Vis. in Eng.

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Background Deep excavations in urban areas have the potential to cause unfavorable effects on ground stability and nearby structures. Thus, it is necessary to evaluate and monitor the environmental impact during deep excavation construction processes. Generally, construction project teams will set up monitoring instruments to control and monitor the overall environmental status, especially during the construction of retaining walls, main excavations, and when groundwater is involved. Large volumes of monitoring data and project information are typically created as the construction project progresses, making it increasingly difficult to manage them comprehensively. Methods To address the abovementioned issues, this project develops a Building Information Model (BIM)-based monitoring system to integrate and visualize monitoring data for risk assessments during urban deep excavation projects. A BIM can be used to establish a comprehensive model of managing a construction project. The system developed herein can access required data from BIM models, and allow complicated numerical data to be displayed effectively in an easily understandable visual format. It is composed of intelligent building components, which includes data attributes and parametric rules for each object. This system can provide a construction project team with a full monitoring view of the ongoing project, along with functions to integrate the information and display it in various ways to present complex engineering monitoring information quickly and clearly. Results Testing examples of the developed system on the excavation of the O6 station in the Kaohsiung metro system in Taiwan are presented to illustrate the improvements of safety management for adjacent structures in urban deep excavation projects. The risks and issues affecting the safety of excavation activities and proximal structures during a project can be identified earlier through effective visualization of information in the system, enabling construction project teams to address them promptly and appropriately by performing accurate risk assessments and decision making. Conclusions This system assists construction project teams in identifying and understanding possible blind spots when attempting to achieve risk assessments during urban deep excavation projects, and further enables the adoption of mitigation measures to reduce risk levels.

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Designing Baiguo Landslide Disaster Monitoring and Pre-warning System Based on GIS

Liu

2013

Geo-Informatics in Resource Management and Sustainable Ecosystem

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Design of GIS-based Monitoring and Early-warning System of Landslide Hazard in Diao Zhongba

Cited by 6 publications

References 1 publication

A Spatiotemporal Fusion Network to Multi Source Heterogeneous Data for Landslide Recognition

A Spatiotemporal Fusion Network to Multi Source Heterogeneous Data for Landslide Recognition

A BIM-based monitoring system for urban deep excavation projects

Designing Baiguo Landslide Disaster Monitoring and Pre-warning System Based on GIS

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