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

journal article in Web of Science

English

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).

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

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

14. 5. 2025

Nature Portfolio

BERLIN

2052-4463

Scientific data

12

1

United Kingdom of Great Britain and Northern Ireland

1

8

8

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

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