Tomasov Adrian, Zaviska Pavel, Dejdar Petr, Klicnik Ondrej, Horvath Tomas, Munster Petr
Brno University of Technology, FEEC, Dept. of Telecommunications, Technicka 12, 61600, Brno, Czech Republic.
Sci Data. 2025 May 14;12(1):793. doi: 10.1038/s41597-025-05088-4.
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).
分布式声学传感（DAS）技术利用光纤来远距离检测声学信号，为地震监测、结构健康监测和安全等应用提供至关重要的高分辨率数据。DAS系统面临的一个重大挑战是对检测到的事件进行准确分类，这对其可靠性至关重要。传统的信号处理方法常常难以处理DAS系统产生的高维、噪声数据，这使得先进的机器学习技术对于改进事件分类至关重要。然而，缺乏大型高质量数据集阻碍了进展。在本研究中，我们展示了一个在大学校园周围收集的DAS测量综合标记数据集，其中包含行走、跑步和车辆移动等事件以及潜在的安全威胁。该数据集为开发和验证机器学习模型提供了宝贵资源，能够实现更准确和自动化的事件分类。通过成功训练卷积神经网络（CNN）证明了该数据集的质量。
