Detail publikačního výsledku

Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems

TOMAŠOV, A.; ZÁVIŠKA, P.; DEJDAR, P.; KLÍČNÍK, O.; HORVÁTH, T.; MÜNSTER, P.

Originální název

Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems

Anglický název

Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems

Druh

Článek WoS

Originální abstrakt

Distributed Acoustic Sensing (DAS) technology leverages optical fibers to detect acoustic signals over long distances, offering high-resolution data critical for applications such as seismic monitoring, structural health monitoring, and security. A significant challenge in DAS systems is the accurate classification of detected events, which is crucial for their reliability. Traditional signal processing methods often struggle with the high-dimensional, noisy data produced by DAS systems, making advanced machine learning techniques essential for improved event classification. However, the lack of large, high-quality datasets has hindered progress. In this study, we present a comprehensive labeled dataset of DAS measurements collected around a university campus, featuring events such as walking, running, and vehicular movement, as well as potential security threats. This dataset provides a valuable resource for developing and validating machine learning models, enabling more accurate and automated event classification. The quality of the dataset is demonstrated through the successful training of a Convolutional Neural Network (CNN).

Anglický abstrakt

Distributed Acoustic Sensing (DAS) technology leverages optical fibers to detect acoustic signals over long distances, offering high-resolution data critical for applications such as seismic monitoring, structural health monitoring, and security. A significant challenge in DAS systems is the accurate classification of detected events, which is crucial for their reliability. Traditional signal processing methods often struggle with the high-dimensional, noisy data produced by DAS systems, making advanced machine learning techniques essential for improved event classification. However, the lack of large, high-quality datasets has hindered progress. In this study, we present a comprehensive labeled dataset of DAS measurements collected around a university campus, featuring events such as walking, running, and vehicular movement, as well as potential security threats. This dataset provides a valuable resource for developing and validating machine learning models, enabling more accurate and automated event classification. The quality of the dataset is demonstrated through the successful training of a Convolutional Neural Network (CNN).

Klíčová slova

Distributed Acoustic Sensing (DAS);Fiber optic sensor;Perimeter security;event classification;phase-OTDR

Klíčová slova v angličtině

Distributed Acoustic Sensing (DAS);Fiber optic sensor;Perimeter security;event classification;phase-OTDR

Autoři

TOMAŠOV, A.; ZÁVIŠKA, P.; DEJDAR, P.; KLÍČNÍK, O.; HORVÁTH, T.; MÜNSTER, P.

Rok RIV

2026

Vydáno

14.05.2025

Nakladatel

Nature Portfolio

Místo

BERLIN

ISSN

2052-4463

Periodikum

Scientific Data

Svazek

12

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

8

Strany počet

8

URL

https://www.nature.com/articles/s41597-025-05088-4

Plný text v Digitální knihovně

http://hdl.handle.net/11012/251323

BibTex

@article{BUT197921,
  author="Adrián {Tomašov} and Pavel {Záviška} and Petr {Dejdar} and Ondřej {Klíčník} and Tomáš {Horváth} and Petr {Münster}",
  title="Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems",
  journal="Scientific Data",
  year="2025",
  volume="12",
  number="1",
  pages="1--8",
  doi="10.1038/s41597-025-05088-4",
  url="https://www.nature.com/articles/s41597-025-05088-4"
}