Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 258-264.doi: 10.11896/jsjkx.201000071

• Big Data & Data Science • Previous Articles     Next Articles

Research on Creep Feature Extraction and Early Warning Algorithm Based on Satellite MonitoringSpatial-Temporal Big Data

LIU Ya-chen1, HUANG Xue-ying2   

  1. 1 Beijing Engineering Research Center for IoT Software and Systems,Beijing University of Technology,Beijing 100124,China
    2 Carnegie Institute of Technology,Carnegie Mellon University ,PA 15213,USA
  • Online:2021-11-10 Published:2021-11-12
  • About author:LIU Ya-chen,born in 1997,postgraduate.Her main research interests include BeiDou high-accuracy position and navigation,and IoT software and system.

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Abstract: This paper presents a new methodology for overcoming the difficulties to issue warning of the occurrence time and tendency of the geologic hazards timely and accurately,employs the latest BeiDou satellite deformation monitoring technology,and considers a novel feature extraction of creep deformation method and algorithm for hazards warning.Based on the data analysis and data cleansing of the data from satellite monitoring spatial-temporal big data,we focus on time and spatial attribute and variation between different monitoring points.Moreover,we extract multi-dimensional of creep deformation such as displacement,displacement angle,instantaneous velocity,acceleration,etc.And the internal variation trend of the monitored data is displayed in a multi-dimensional manner.Research on creep disaster warning algorithm can find and warn potential disasters in deformation process,this finding is helpful to take measures to ensure the personal and property safety timely.Our research findings have important application value and theoretical significance in many fields.

Key words: Creep feature extraction, Early warning algorithm, Landslide geological hazard, Satellite monitoring, Spatial-temporal big data

CLC Number: 

  • TP391
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