Multialtitude, Multimodal Maritime Surveillance System

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Authors: Thanet Markchom, Olympia Kourounioti, Matteo Marturini, Romaios Bratskas, Kilian Wohlleben, Jonathan Boyle, Lulu Chen, George Voskopoulos, Christos Kontopoulos, Stephan Veigl, Andreas Opitz, Anastasios Gkamaris
+5 more Dimitris Papachristos, Dumitru Lunic, James M. Ferryman, Andreas Kriechbaum-Zabini, George Leventakis
Cited by: 1
References: 52

Abstract

Maritime surveillance plays a vital role in protecting coastal and maritime environments. However, traditional maritime surveillance systems that rely on single-altitude, single-modality sensors suffer from limited coverage and sensitivity to weather conditions. To address these limitations, this paper presents a comprehensive maritime surveillance system that integrates multi-altitude, multimodal sensor platforms, including ground-based sensors, low-altitude UAVs, and satellites, for maritime threat detection. Each platform is equipped with dedicated modules for object detection, tracking, and geolocation, leveraging its unique sensing capabilities to contribute to a coordinated surveillance system. Moreover, a novel Multi-Altitude, Multimodal Maritime Surveillance (MAMMS) dataset is introduced. This dataset includes data from these sensor types, enabling rigorous benchmarking across varying operational conditions. Experimental results indicate that the system achieved an average mAP of 50.5% across all sensors in object detection, surpassing state-of-the-art models in most cases. For object tracking, the system achieved an average IDF1 of 0.263 and a HOTA of 0.297, comparable to state-of-the-art methods, while exhibiting substantially fewer average ID switches (75.46) compared to the strongest baseline (301.46). For geolocation approximation, the system achieved an error of less than 11m in certain scenarios. A case study was also conducted to assess the sensor platforms when integrated into a multi-sensor fusion system. The case study showed that complementary information from different platforms can help reduce false alarms and improve object geolocation accuracy. The dataset is available at https://zenodo.org/records/17979190.

Fields of Study & Subjects

Maritime Navigation and Safety Oil Spill Detection and Mitigation Infrared Target Detection Methodologies

Publication details

Journal / source
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
26
Issue
5
Pages
7101–7119
Publication date
13 January 2026
Language
EN