Multi-Sensor Inferred Trajectories (MUSIT) for vessel mobility

Ray C., Troupiotis-Kapeliaris A., Kontopoulos I., Andronikou V., Nasios I., Piliouras N., Chevallier T., Delmas V., Tserpes K., Zissis D., Renso C., Carlini E.

Maritime information management, Marine GIS and data fusion, Trajectory inference and mining 

The abundance of tracking sensors in recent years has led to the generation of high-frequency and high-volume streams of data, including vessel locations, marine observations captured from many sensors (living resources, sea state, weather conditions, etc.). However, there are cases where the trajectory of a moving object has gaps, errors, or is unavailable. Thus, while a vast pool of tracking data is available, these data remain unexplored or underutilized and have the potential to reveal important information. The MUlti-Sensor Inferred Trajectories (MUSIT) project aims to explore and fuse data from all heterogeneous sources to provide detailed information about the location and behavior of a moving object, reduce gaps, and produce a refined and inferred trajectory with minimal errors. The fusion of multi-sensor data is required to fill in the trajectory gaps of moving objects and attach useful semantics to the trajectory. Artificial intelligence algorithms and spatiotem-poral methodologies that can fuse information and infer missing knowledge are also crucial. Furthermore, different representation models from multiple sensors will also be explored. Multi-sensor datasets will be designed and made available to experiment with models, fusion and trajectory inference algorithms, and deduce new knowledge. Therefore, the MUSIT project will tackle these issues in a three-step process: i) data collection and creation, ii) exploitation and utilization of cross-domain representation models for trajectories, and iii) analysis and processing of outcomes to produce information-rich results related to vessel monitoring

Publisher: IEEE

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