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

This dataset was collected using a Distributed Acoustic Sensing (DAS) system with phase-sensitive Optical Time-Domain Reflectometry (Φ-OTDR) technology. It includes labeled and unlabeled acoustic signal measurements gathered around a university campus, covering activities such as walking, running, vehicular movement, and potential security threats like fiber manipulation and fence climbing. The data was captured using an Optasense ODH-F DAS interrogator, which monitors signals from a buried single-mode fiber optic cable. The dataset, stored in HDF5 format, serves as a critical resource for training machine learning models aimed at event classification in DAS systems. Each event is identified by power spectral density (PSD) representations and labeled accordingly. This dataset is ideal for researchers developing and validating machine learning algorithms for DAS-based applications, including structural health monitoring and perimeter security.