Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques

TL;DR

This paper reviews AI and data fusion techniques essential for integrating space-based sensors into comprehensive maritime surveillance systems, enhancing coverage and operational effectiveness.

Contribution

It introduces advanced AI and data fusion methods tailored for processing heterogeneous space-based maritime surveillance data, advancing the integration of multiple sensor sources.

Findings

Identification of promising AI techniques for maritime data analysis

Development of data fusion strategies for heterogeneous sensor integration

Enhanced maritime situational awareness through integrated data processing

Abstract

Maritime surveillance (MS) is of paramount importance for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since ground-based radars and automatic identification system (AIS) do not always provide a comprehensive and seamless coverage of the entire maritime domain, the use of space-based sensors is crucial to complement them. We reviewed space-based technologies for MS in the first part of this work, titled "Space-based Global Maritime Surveillance. Part I: Satellite Technologies" [1]. However, future MS systems combining multiple terrestrial and space-based sensors with additional information sources will require dedicated artificial intelligence and data fusion techniques for the processing of raw satellite images and fuse heterogeneous information. The second part of our work focuses on the most promising artificial intelligence and data fusion techniques for MS using space-based sensors.