The Parallel and Precision Adaptive Method of Marine Lane Extraction Based on QuadTree

  • Zhuoran Li
  • Guiling WangEmail author
  • Jinlong Meng
  • Yao Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)


Extracting the marine lane results from the ocean spatial big data is a challenging problem. One of the challenges is that the quality of the trajectory data is quite low, and the trajectory data quality is extremely different in different areas. A parallel and precision adaptive method of marine lane extraction based on QuadTree is proposed to meet this challenge. The method takes advantage of several methods including average sampling, interpolation, removing noise, trajectory segmentation, and trajectory clustering based on GeoHash encoding through the MapReduce parallel computing framework. The preprocessing phase can effectively simplify the big data and improve the efficient of data processing. Based on the QuadTree data structure, a parallel merge filtering algorithm is proposed and implemented used Spark framework. The algorithm performs grid merging on the sparse grid regions, and obtaining a new grid result with different size. The sliding local window filtering algorithm based on the QuadTree is proposed to obtain the marine lane grid set data. Applying the Delaunay triangulation method on the grid data, the multi-precision marine lane results are effectively extracted. The experimental results show that the proposed method can automatically extract multi-precision marine lane using the trajectory data near the coast with high and low grid precision.


AIS data Precision adaptive Marine lane extraction 



This work is supported by Beijing Natural Science Foundation No. 4172018, National Natural Science Foundation of China No. 61832004, No. 61672042, and University Cooperation Projects Foundation of CETC Ocean Corp.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Zhuoran Li
    • 1
    • 2
  • Guiling Wang
    • 1
    • 2
    Email author
  • Jinlong Meng
    • 1
  • Yao Xu
    • 2
  1. 1.Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream DataNorth China University of TechnologyBeijingChina
  2. 2.Ocean Information Technology Company, China Electronics Technology Group Corporation (CETC Ocean Corp.)BeijingChina

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