Machine Learning for Distributed Acoustic and Fibre-Optic Sensing in Infrastructure Monitoring: A Systematic Review (2015-2025)

Abstract

Distributed acoustic sensing (DAS), together with broader distributed fibre-optic sensing (DFOS), has emerged over the past decade as a candidate technology for large-scale infrastructure monitoring, while machine learning (ML) is increasingly used to interpret its high-throughput data. This study presents a PRISMA 2020-guided bibliometric and systematic-mapping review of this convergence. A structured search of Web of Science, Scopus, and IEEE Xplore returned 291 records published between 2015 and 2025; after duplicate removal and two stages of screening, a final analytical corpus of 31 publications was established, of which 16 are primary DAS-with-ML infrastructure studies. Publication output rises sharply to seven papers in 2024 and twelve in 2025. Among the primary studies, convolutional neural networks (CNNs), recurrent models, and CNN-LSTM hybrids are prominent, with more recent use of few-shot learning, graph neural networks (GNNs), generative models, and self-supervised learning. Perimeter security is the most represented application domain, followed by traffic, railway, and structural-health monitoring. Critical synthesis shows that the apparent dominance of CNNs is partly explained by the natural two-dimensional spatiotemporal representation of DAS data and by mature, reusable CNN pipelines, rather than evidence of universal superiority. Cross-study comparison remains limited by heterogeneous sensing environments, inconsistent acquisition and label reporting, non-uniform evaluation protocols, sparse computational reporting, and minimal long-term robustness evaluation. Five research gaps are identified: cross-infrastructure generalisation, standardised benchmarks and open datasets, physics-data-driven integration, interpretability and uncertainty, and operationally robust edge deployment.