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

arXiv: https://arxiv.org/abs/2011.11338 · HTML: https://mlsanity.com/paper/2011.11338/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction](https://mlsanity.com/paper/2109.03958/) (2021)
- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)
- [Large Deviations for Classification Performance Analysis of Machine Learning Systems](https://mlsanity.com/paper/2301.07104/) (2023)
- [Extended target tracking utilizing machine-learning software -- with applications to animal classification](https://mlsanity.com/paper/2310.08316/) (2023)
- [Outlier detection in maritime environments using AIS data and deep recurrent architectures](https://mlsanity.com/paper/2406.09966/) (2024)
- [Preventing Adversarial AI Attacks Against Autonomous Situational Awareness: A Maritime Case Study](https://mlsanity.com/paper/2505.21609/) (2025)

## Abstract (arXiv)

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.