Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

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In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.@en

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi's trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China.@en