Cartographic sounding selection is a constraint-based bathymetric generalization process for identifying navigationally relevant soundings for nautical chart display.Electronic Navigational Charts(ENCs)are the premier...Cartographic sounding selection is a constraint-based bathymetric generalization process for identifying navigationally relevant soundings for nautical chart display.Electronic Navigational Charts(ENCs)are the premier maritime navigation medium and are produced according to international standards and distributed around the world.Cartographic generalization for ENCs is a major bottleneck in the chart creation and update process,where high volumes of data collected from constantly changing seafloor topographies require tedious examination.Moreover,these data are provided by multiple sources from various collection platforms at different levels of quality,further complicating the generalization process.Therefore,in this work,a comprehensive sounding selection algorithm is presented that focuses on safe navigation,leveraging both the Digital Surface Model(DSM)of multi-source bathymetry and the cartographic portrayal of the ENC.A taxonomy and hierarchy of soundings found on ENCs are defined and methods to identify these soundings are employed.Furthermore,the significant impact of depth contour generalization on sounding selection distribution is explored.Incorporating additional ENC bathymetric features(rocks,wrecks,and obstructions)affecting sounding distribution,calculating metrics from current chart products,and introducing procedures to correct cartographic constraint violations ensures a shoal-bias and mariner-readable output.This results in a selection that is near navigationally ready and complementary to the specific waterways of the area,contributing to the complete automation of the ENC creation and update process for safer maritime navigation.展开更多
With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,l...With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.展开更多
基金supported by the National Oceanic and Atmospheric Administration under[grant number NA20NOS4000196]partially supported by the National Science Foundation under[grant number 1910766].
文摘Cartographic sounding selection is a constraint-based bathymetric generalization process for identifying navigationally relevant soundings for nautical chart display.Electronic Navigational Charts(ENCs)are the premier maritime navigation medium and are produced according to international standards and distributed around the world.Cartographic generalization for ENCs is a major bottleneck in the chart creation and update process,where high volumes of data collected from constantly changing seafloor topographies require tedious examination.Moreover,these data are provided by multiple sources from various collection platforms at different levels of quality,further complicating the generalization process.Therefore,in this work,a comprehensive sounding selection algorithm is presented that focuses on safe navigation,leveraging both the Digital Surface Model(DSM)of multi-source bathymetry and the cartographic portrayal of the ENC.A taxonomy and hierarchy of soundings found on ENCs are defined and methods to identify these soundings are employed.Furthermore,the significant impact of depth contour generalization on sounding selection distribution is explored.Incorporating additional ENC bathymetric features(rocks,wrecks,and obstructions)affecting sounding distribution,calculating metrics from current chart products,and introducing procedures to correct cartographic constraint violations ensures a shoal-bias and mariner-readable output.This results in a selection that is near navigationally ready and complementary to the specific waterways of the area,contributing to the complete automation of the ENC creation and update process for safer maritime navigation.
文摘With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.
