| Spatiotemporal big data mining is one of the hot topics in the field of data mining in recent years.It is of great significance to extract road information from traffic data for the study of spatiotemporal data.Unlike extracting the land road,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.1).The method takes advantage of several methods including removing noise,interpolation,trajectory segmentation,average sampling,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.2).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 using tanker ships data of June,2016 can achieve an accuracy rate of 89.7%and a recall rate of 89.3%.Experiments shows that AdaptiveNiBlack is higher than OTSU and NiBlack in precision rate and recall rate.3).The method realized the experimental verification system of marine lane extraction,and provided the corresponding user operation interface,which can automatically extract the marine lane results with different precision from the ship traj ectory big data covering different qualitys in the offshore and nearshore areas. |
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