The design and compilation of comprehensive maritime atlas integrating sea and land information serve the national development initiative of "building a marine community with shared future", it is also an important thrust to promote the culture fusion and friendly cooperation between marine and land areas. Therefore, it has attracted widespread attention in the field of cartography. This paper firstly discusses the special characteristics of marine-land integrated atlas through the comparative analysis with nautical charts and land atlas, referring to the thematic topic, map scale, map projection, and geographic subdivision. Then it proposes a top-level design of fundamental geographic framework for the comprehensive maritime atlas from the perspective of marine and land combination, including both the content system and construction strategy. Next, taking "Maritime Atlas of the World" as an instance, the paper puts forward the specific establishment of fundamental geographic framework in details: (1) the coordinate system of WGS84 should be used for medium and small scale maps, while the CGCS2000 should be used for large scale maps; (2) according to the map thematic and content, cartographic area, and deformation needs, mapping units can use various projections such as qual difference latitude parallel polyconic projection, Mercator projection, Goode projection, and so on; (3) six types of geographic base maps are adopted, i.e. typical reginal map, land map, sea map, navigation area map, and port map, their contents are generated in series by further selection and simplification; (4) all the mapping units should be arranged to the north, and map layouts are various with flexible geographic subdivision and map subdivision. The “Maritime Atlas of the world” complication practice shows that this proposed fundamental geographic framework can well support the integration of marine and land information into a cohesive coordinative manner.

Harbor detection is the top priority of maritime ship supervision, and the ship activity information acquired by Automatic Identification System (AIS) can provide high temporal and spatial accuracy of ship activity information for harbor detection. In order to explore the application of AIS data in harbor detection, a harbor detection method based on multi-source data and semantic modeling of ship stop trajectory is proposed. Firstly, the semantic model of ship stop trajectory is constructed through data mining and semantic information enhancement to identify ship stop trajectory in the harbor area. Secondly, a classification model based on random forest is established to classify ship berthing trajectories and ship anchoring trajectories, and then harbor berths and anchorages are extracted by using spatial step-by-step merging method. Finally, the data of ship berthing trajectories, roads, coastline, bathymetry, and land use and land cover data are integrated to identify harbor objects considering situational-domain knowledge. Based on over 83 million AIS trajectory records of 96,790 ships in 2017, the proposed method is applied to detect harbor object in the South China Sea study area. The experimental results show that the overall classification accuracy of ship stop behavior is 0.9477 and the Kappa coefficient is 0.8948. 447 harbor areas in the South China Sea study area are extracted, and the overlay verification results with Google Earth images show that the extraction results are all located within the real harbor images. In addition, compared with the 24 harbor locations in the South China Sea region contained in the Natural Earth dataset, the integrity of the extraction results is greatly enhanced. Therefore, the harbor detection method based on multi-source data and semantic modeling of ship stop trajectory has high accuracy and completeness for harbor detection. Meanwhile, the harbor areas extracted by this method can provide target areas for harbor identification based on remote sensing images, thus improving the efficiency of dynamic identification of harbor object in a large region or even globally.

Safety analysis based on the spatial distribution of maritime traffic accidents is of great significance for maritime traffic safety management. In this paper, we obtained the potential risk areas of maritime traffic accidents in Xiamen waters through extraction, analysis, and prediction of spatial distribution of maritime traffic accident data in Xiamen Port from 2008 to 2020. The original accident data were firstly used for spatial orientation in GIS software to form a visualized spatial distribution map of accident points. Then, the areas where maritime traffic accidents frequently occur were identified using the kernel density analysis. In addition, the spatial autocorrelation analysis method was employed to obtain the spatial distribution characteristics and specific gathering points of accidents in this region. Finally, we performed standardized grid segmentation on the data in the target waters based on the spatial distribution characteristics and predicted potential risk areas of maritime traffic accidents using a machine learning algorithm (i.e., random forest). The kernel density analysis results showed that Xiamen Bay and Western Sea were high risk areas in terms of accident frequency. Moreover, the spatial autocorrelation analysis results indicated that in terms of spatial distribution characteristics, the spatial distribution of Xiamen Port showed aggregation characteristics and was positively correlated with time. For specific spatial gathering points of accidents, Xiamen Bay and Western Sea were still the centers with a high incidence of accidents. Furthermore, the risk prediction model of Xiamen Bay and its surrounding waters demonstrated that potential risk areas of maritime traffic accidents were mostly located at the intersection of coastal and estuary areas, because there were frequent crossings of vessels and fishing boats. The results of this study show that based on the distribution characteristics extracted and grid analysis of geospatial data, our proposed method has a good effect in predicting maritime traffic accidents by use of machine learning methods.

Aiming at the problem of insufficient time for large vessels to enter the port by tide in the long channel, this paper proposed a refined segmented tide riding model for large vessels based on multi-source maritime data such as AIS data, port tide data, electronic chart data, and so on. Firstly, based on the AIS data, the K-medoids algorithm was used to mine the characteristics of the vessel's tide riding behavior, identify the key points of the vessel's tide riding trajectory, and calculate the key vessel position points of the vessel's tide-riding behavior change. Then, combing the geographical environment characteristics of the long channel and the characteristics of the navigation behavior of the vessels, the long channel was refined and segmented. Based on the port tide data, a refined calculation model of the vessel's segmented tide riding window period was constructed. Secondly, we designed an adaptive arrangement algorithm for the tide riding duration to solve the longest window period of the vessel's tide riding. Taking the Huanghua Port integrated port area as an example, the refined segmented tide riding model proposed in this paper was verified. Finally, based on the electronic chart data, the geographic information system platform was used to realize the fine segmented three-dimensional (3D) dynamic deduction of the vessel's tide riding, to further verify the safety of the vessel's tide navigation. The results show that the model can effectively increase the window period for vessels to enter the port by tide and improve the efficiency of ships entering the port by tide. This study can provide theoretical guidance for the port and shipping management departments to formulate vessel entry plans.

With the proposal of "carbon peak" and "carbon neutralization", Liquefied Natural Gas (LNG) has gradually garnered the attention of energy market as a clean and low-carbon energy. In this context, it is of great significance to analyze the evolution mode of the LNG maritime transport network, so as to master the dynamic of global energy pattern and the status of China's import trade. In this paper, the evolution trend of the global LNG maritime transport network from 2018 to 2020 is explored based on the ship trajectory data and complex network theory. Meanwhile, according to China's trade status, LNG import sources, distribution of main import ports, and the inflow status of the top three import ports in China are analyzed. The results show that: (1) From 2018 to 2020, the global LNG maritime transport network expanded with a “scale-free” characteristic. The "breadth" and "depth" of node connections in the backbone network are increasing, and there is a risk that global LNG trade will become monopolistic; (2) The countries along the "Belt and Road Initiative" actively participated in trade. The numbers of import ports and import voyages in Central and North America, South and Southeast Asia have significantly increased, and in particular, Sabetta and Bonny ranked the top eight globally according to their export volume; (3) The average shortest path length of the network is increasing year by year from 2018 to 2020, and the new mode of "transshipment port" business is gradually emerging. By 2020, 21 transshipment ports have participated in LNG trade, and the United States occupies the dominant position in global transshipment; (4) In recent three years, China's LNG import scale has developed rapidly, and the flow direction of the maritime transport network tends to be diversified. However, Australia is still the main LNG source for China. In terms of import volume, the ports of Tianjin, Shenzhen, and Yung'an rank the top three in China, and the pressure to reduce carbon emissions has prompted the economically developed regions to build terminals and increase imports.

Inland river ports are important nodes of inland river shipping links. Evaluation of the regional advantage of inland river ports is of great significance for port infrastructure construction in the Yangtze River Economic Belt. Taking the inland ports along the Yangtze River in 2021 as the research object, the original model is improved by using multi-source data and combining three indicators within the radiation range of inland ports on the Yangtze River. The AHP-EWM model is used to calculate the location advantage of 28 ports along the Yangtze River in the Yangtze River economic belt. Results show that: (1) except Chongqing and Wuhan, the density of traffic network in the port radiation range beyond Jiangsu province is relatively low. From the perspective of the influence of traffic networks, its distribution is relatively balanced, and the value is relatively similar. The influence of the spatial distribution of urban economics is disordered; (2) The spatial pattern of freight ports in the Yangtze River Economic Belt is characterized by "three centers, one cluster" according to their regional advantages and spatial distribution; (3) By dividing inland ports at the provincial level along the Yangtze River, it is found that the regional difference of dominance between the three ports in Jiangxi Province is the smallest, and the overall degree of dominance is also low. The result of Anhui Province is similar to Jiangxi Province. The overall degree of dominance of ports in Jiangsu Province is high with a small regional difference. The regional dominance of ports in Hubei Province has the largest difference, indicating that there might be issues such as unbalanced infrastructure construction that needs to be improved in future. The research results can provide guidance for infrastructure construction, road traffic planning, and port site selection of various ports in the Yangtze River Economic Belt, and help open up the "last kilometer" of railway and high-grade highway access to ports.