Publication detail
Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems
TOMAŠOV, A. ZÁVIŠKA, P. DEJDAR, P. KLÍČNÍK, O. HORVÁTH, T. MÜNSTER, P.
Original Title
Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems
Type
journal article in Web of Science
Language
English
Original Abstract
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).
Keywords
Distributed Acoustic Sensing (DAS);Fiber optic sensor;Perimeter security;event classification;phase-OTDR
Authors
TOMAŠOV, A.; ZÁVIŠKA, P.; DEJDAR, P.; KLÍČNÍK, O.; HORVÁTH, T.; MÜNSTER, P.
Released
14. 5. 2025
Publisher
Nature Portfolio
Location
BERLIN
ISBN
2052-4463
Periodical
Scientific data
Year of study
12
Number
1
State
United Kingdom of Great Britain and Northern Ireland
Pages from
1
Pages to
8
Pages count
8
URL
Full text in the Digital Library
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="8",
doi="10.1038/s41597-025-05088-4",
issn="2052-4463",
url="https://www.nature.com/articles/s41597-025-05088-4"
}