DASBoot: an annotation toolkit for DAS-based maritime surveillance

Angel Bueno Rodriguez; Felix Sattler; Tino Flenker; Enno Peters; Sarah Barnes; Maurice Stephan

doi:10.1117/12.3031529

13 November 2024 DASBoot: an annotation toolkit for DAS-based maritime surveillance

Angel Bueno Rodriguez, Felix Sattler, Tino Flenker, Enno Peters, Sarah Barnes, Maurice Stephan

Proceedings Volume 13206, Artificial Intelligence for Security and Defence Applications II; 1320609 (2024) https://doi.org/10.1117/12.3031529
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Abstract

Maritime surveillance relies on advanced technologies to ensure the safety and security of national and international waters, particularly in monitoring vessel activities. Distributed Acoustic Sensing (DAS) has emerged as a powerful technology for detecting and analyzing underwater acoustic signatures along fiber-optic cables. However, the lack of annotated DAS datasets in maritime contexts, combined with the high dimensionality and unstructured nature of recorded data streams, hinders the deployment of automated solutions that rely on labeled data for vessel detection. This work introduces DASBoot, a novel annotation toolkit designed to enhance maritime surveillance by aligning vessel signatures from DAS data with Automatic Identification System (AIS) messages. Our approach integrates data processing, fusion, and visualization within a cohesive workflow that significantly reduces the cognitive load on analysts while improving the accuracy of vessel identification. The experimental results demonstrate the effectiveness of our method for dataset annotation and pave the way for future advancements in DAS-based automated maritime surveillance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Angel Bueno Rodriguez, Felix Sattler, Tino Flenker, Enno Peters, Sarah Barnes, and Maurice Stephan "DASBoot: an annotation toolkit for DAS-based maritime surveillance", Proc. SPIE 13206, Artificial Intelligence for Security and Defence Applications II, 1320609 (13 November 2024); https://doi.org/10.1117/12.3031529

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