{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T12:29:23Z","timestamp":1774009763431,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper studies an advanced machine learning method, specifically few-shot classification with meta-learning, applied to distributed acoustic sensing (DAS) data. The study contributes two key aspects: (i) an investigation of different pre-processing methods for DAS data and (ii) the implementation of a neural network model based on meta-learning to learn a representation of the processed data. In the context of urban infrastructure monitoring, we develop a few-shot classification framework that classifies query samples with only a limited number of support samples. The model consists of an embedding network trained on a meta dataset for feature extraction and is followed by a classifier for performing few-shot classification. This research thoroughly explores three types of data pre-processing, that is, decomposed phase, power spectral density, and frequency energy band, as inputs to the neural network. Experimental results show the efficient learning capabilities of the embedding model when working with various pre-processed data, offering a range of pre-processing options. Furthermore, the results demonstrate outstanding few-shot classification performance across a large number of event classes, highlighting the framework\u2019s potential for urban infrastructure monitoring applications.<\/jats:p>","DOI":"10.3390\/s24010049","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T03:36:17Z","timestamp":1703129777000},"page":"49","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Few-Shot Classification with Meta-Learning for Urban Infrastructure Monitoring Using Distributed Acoustic Sensing"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4562-9406","authenticated-orcid":false,"given":"Huynh Van","family":"Luong","sequence":"first","affiliation":[{"name":"AP Sensing GmbH, Herrenberger Str. 130, 71034 B\u00f6blingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9300-5860","authenticated-orcid":false,"given":"Nikos","family":"Deligiannis","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium"},{"name":"Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, B-3001 Leuven, Belgium"}]},{"given":"Roman","family":"Wilhelm","sequence":"additional","affiliation":[{"name":"AP Sensing GmbH, Herrenberger Str. 130, 71034 B\u00f6blingen, Germany"}]},{"given":"Bernd","family":"Drapp","sequence":"additional","affiliation":[{"name":"AP Sensing GmbH, Herrenberger Str. 130, 71034 B\u00f6blingen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"ref_1","first-page":"3243","article-title":"Distributed Vibration Sensor Based on Coherent Detection of Phase-OTDR","volume":"28","author":"Lu","year":"2010","journal-title":"J. Light. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liang, K., Ye, Q., Cai, H., Qu, R., and Fang, Z. (2011, January 13\u201316). Phase-sensitive OTDR system based on digital coherent detection. Proceedings of the Asia Communications and Photonics Conference and Exhibition, Shanghai, China.","DOI":"10.1364\/ACP.2011.83110S"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2019.2895249","article-title":"Pushing the Reach of Fiber Distributed Acoustic Sensing to 125 km Without the Use of Amplification","volume":"3","author":"Cedilnik","year":"2019","journal-title":"IEEE Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gorshkov, B.G., Y\u00fcksel, K., Fotiadi, A.A., Wuilpart, M., Korobko, D., Zhirnov, A.A., Stepanov, K.V., Turov, A.T., Konstantinov, Y.A., and Lobach, I.A. (2022). Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective. Sensors, 22.","DOI":"10.3390\/s22031033"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_6","first-page":"102080G","article-title":"Deep learning based multi-threat classification for phase-OTDR fiber optic distributed acoustic sensing applications","volume":"10208","author":"Aktas","year":"2017","journal-title":"Fiber Opt. Sens. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4755","DOI":"10.1109\/JLT.2019.2919713","article-title":"Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach","volume":"37","author":"Shiloh","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_8","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1364\/OE.28.002925","article-title":"Fiber distributed acoustic sensing using convolutional long short-term memory network: A field test on high-speed railway intrusion detection","volume":"28","author":"Li","year":"2020","journal-title":"Opt. Express"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27277","DOI":"10.1364\/OE.397509","article-title":"Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions","volume":"28","author":"Peng","year":"2020","journal-title":"Opt. Express"},{"key":"ref_11","first-page":"3371","article-title":"A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data","volume":"34","author":"Lior","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ge, Z., Wu, H., Zhao, C., and Tang, M. (2022). High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time\u2013Space Analysis. Sensors, 22.","DOI":"10.3390\/s22052053"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bublin, M. (2021). Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches. Sensors, 21.","DOI":"10.3390\/s21227527"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xie, Y., Wang, M., Zhong, Y., Deng, L., and Zhang, J. (2023). Label-Free Anomaly Detection Using Distributed Optical Fiber Acoustic Sensing. Sensors, 23.","DOI":"10.3390\/s23084094"},{"key":"ref_15","unstructured":"Raghu, A., Bengio, S., and Vinyals, O. (2020, January 26\u201330). Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., and Isola, P. Rethinking Few-Shot Image Classification: A Good Embedding Is All You Need? In Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23\u201328 August 2020.","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automated Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1006\/acha.1997.0238","article-title":"Wavelet transforms that map integers to integers","volume":"5","author":"Calderbank","year":"1998","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016, January 19\u201322). Wide Residual Networks. Proceedings of the British Machine Vision Conference, York, UK.","DOI":"10.5244\/C.30.87"},{"key":"ref_21","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_22","first-page":"4080","article-title":"Prototypical Networks for Few-shot Learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. 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