Space-based Global Maritime Surveillance. Part I: Satellite Technologies
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Abstract (arXiv)
Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 3; 2025: 0; 2026: 2.
8 papers.
- 2022The State of Aerial Surveillance: A Surveycited by 20
- 2024AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identificationcited by 19
- 2022Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysiscited by 6
- 2024M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Datacited by 6
- 2023Large Deviations for Classification Performance Analysis of Machine Learning Systemscited by 0
- 2024Outlier detection in maritime environments using AIS data and deep recurrent architecturescited by 0
- 2026Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Gridscited by 0
- 2026QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authenticationcited by 0
- 2026QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication20 Aug 2026
- 2026Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids30 Mar 2026
- 2024Outlier detection in maritime environments using AIS data and deep recurrent architectures14 Jun 2024
- 2024M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data6 Jun 2024
- 2024AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification5 Jan 2024
- 2023Large Deviations for Classification Performance Analysis of Machine Learning Systems16 Jan 2023
- 2022Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis22 Jul 2022
- 2022The State of Aerial Surveillance: A Survey9 Jan 2022