Integrating GPU-Accelerated for Fast Large-Scale Vessel Trajectories Visualization in Maritime IoT Systems

Abstract

With the advancement of satellite communication technology, the maritime Internet of Things (IoT) has made significant progress. As a result, vast amounts of Automatic Identification System (AIS) data from global vessels are transmitted to various maritime stakeholders through Maritime IoT systems. AIS data contains a large amount of dynamic and static information that requires effective and intuitive visualization for comprehensive analysis. However, two major deficiencies challenge current visualization models: a lack of consideration for interactions between distant pixels and low efficiency. To address these issues, we developed a large-scale vessel trajectories visualization algorithm, called the Non-local Kernel Density Estimation (NLKDE) algorithm, which incorporates a non-local convolution process. It accurately calculates the density distribution of vessel trajectories by considering correlations between distant pixels. Additionally, we implemented the NLKDE algorithm under a Graphics Processing Unit (GPU) framework to enable parallel computing and improve operational efficiency. Comprehensive experiments using multiple vessel trajectory datasets show that the NLKDE algorithm excels in vessel trajectory density visualization tasks, and the GPU-accelerated framework significantly shortens the execution time to achieve real-time results. From both theoretical and practical perspectives, GPU-accelerated NLKDE provides technical support for real-time monitoring of vessel dynamics in complex water areas and contributes to constructing maritime intelligent transportation systems. The code for this paper can be accessed at: https://github.com/maohliang/GPU-NLKDE.