{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:08:13Z","timestamp":1779361693873,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031756221","type":"print"},{"value":"9783031756238","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-75623-8_25","type":"book-chapter","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T19:16:40Z","timestamp":1735845400000},"page":"318-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning for the Classification of Ports in Maritime Transport Statistics via AIS Data"],"prefix":"10.1007","author":[{"given":"A.","family":"Pappagallo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Ortame","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Massacci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Sisti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Pugliese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Varlamis, I., Tserpes, K., Sardianos, C.: Detecting search and rescue missions from AIS data. In: 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), pp. 60\u201365, April 2018. iSSN: 2473\u20133490","DOI":"10.1109\/ICDEW.2018.00017"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data, arXiv:2001.03093 [cs], January 2021, arXiv: 2001.03093","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Murray, B., Perera, L.P.: An AIS-based deep learning framework for regional ship behavior prediction, Reliability Engineering & System Safety, p. 107819, May 2021","DOI":"10.1016\/j.ress.2021.107819"},{"issue":"4","key":"25_CR4","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1017\/S0373463308004888","volume":"61","author":"Z Ou","year":"2008","unstructured":"Ou, Z., Zhu, J.: AIS database powered by GIS technology for maritime safety and security. J. Navig. 61(4), 655\u2013665 (2008)","journal-title":"J. Navig."},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"De Cubber, Geert, et al.: Distributed coverage optimisation for a fleet of unmanned maritime systems. ACTA IMEKO 10.3 pp. 36\u201343 (2021)","DOI":"10.21014\/acta_imeko.v10i3.1031"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Tu, E., et al.: Exploiting AIS data for intelligent maritime navigation: a compre-hensive survey from data to methodology. IEEE Trans. Intell. Transp. Syst. 19(5), 1559\u20131582 (2017)","DOI":"10.1109\/TITS.2017.2724551"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Mou, J.M., Van der Tak, C., Ligteringen, H.: \u201cStudy on collision avoidance in busy waterways by using AIS data. Ocean Eng. 37(5\u20136), 483\u2013490 (2010)","DOI":"10.1016\/j.oceaneng.2010.01.012"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Soldi, G., et al.: Space-based global maritime surveillance. Part II: Artificial in-telligence and data fusion techniques. IEEE Aerospace Electron. Syst. Mag. 36(9), 30\u201342 (2021)","DOI":"10.1109\/MAES.2021.3070884"},{"key":"25_CR9","unstructured":"Ristic, B., La Scala, B., Morelande, M., Gordon, N.: Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction. In: 2008 11th International Conference on Information Fusion, pp. 1\u20137, June 2008"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Capobianco, S., Millefiori, L.M., Forti, N., Braca, P., Willett, P.: Deep learning methods for vessel trajectory prediction based on recurrent neural networks. IEEE Trans. Aerospace Electron. Syst. p. 1 (2021), conference Name: IEEE Transactions on Aerospace and Electronic Systems","DOI":"10.1109\/TAES.2021.3096873"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially ac-ceptable trajectories with generative adversarial networks, pp. 2255\u20132264 (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"25_CR12","unstructured":"H3 (Hexagonal hierarchical geospatial indexing system), https:\/\/h3geo.org\/"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Wang, C., Ren, H., Li, H.: Vessel trajectory prediction based on AIS data and bidi-rectional GRU. In: 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), pp. 260\u2013264, July 2020","DOI":"10.1109\/CVIDL51233.2020.00-89"},{"key":"25_CR14","unstructured":"N\u00e6ss, P.A.: Investigation of multivariate freight rate prediction using machine learning and AIS data. MS thesis. NTNU (2018)"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"D\u00fcz, B., van Iperen, E.: Ship trajectory prediction using encoder\u2013decoder-based deep learning models. J. Location Based Services, pp. 1\u201321 (2024)","DOI":"10.1080\/17489725.2024.2306339"},{"key":"25_CR16","doi-asserted-by":"publisher","first-page":"108956","DOI":"10.1016\/j.oceaneng.2021.108956","volume":"228","author":"DW Gao","year":"2021","unstructured":"Gao, D.W., Zhu, Y.S., Zhang, J.F., He, Y.K., Yan, K., Yan, B.R.: A novel MP-LSTM method for ship trajectory prediction based on AIS data. Ocean Eng. 228, 108956 (2021)","journal-title":"Ocean Eng."},{"key":"25_CR17","unstructured":"Frank, R.: The perceptron: a theory of statistical separability in cognitive systems. United States department of commerce (1958)"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"25_CR19","unstructured":"Olah, C.: Understanding LSTM networks (2015)"},{"key":"25_CR20","unstructured":"Yoon, K.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)"},{"key":"25_CR21","unstructured":"UN Global Platform. https:\/\/unstats.un.org\/bigdata\/un-global-platform.cshtml"},{"key":"25_CR22","unstructured":"White, T.: Hadoop: The definitive guide. O\u2019Reilly Media, Inc. (2012)"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Salloum, S., et al.: Big data analytics on Apache Spark. Int. J. Data Sci. Anal. 1, 145\u2013164 (2016)","DOI":"10.1007\/s41060-016-0027-9"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Shvachko, K., et al.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), IEEE (2010)","DOI":"10.1109\/MSST.2010.5496972"},{"key":"25_CR25","unstructured":"Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for In-Memory cluster computing. In: 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12) (2012)"},{"key":"25_CR26","unstructured":"Lloyd\u2019s Register, https:\/\/www.lr.org"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Lv, T., Tang, P., Zhang, J.: A real-time AIS data cleaning and indicator analysis algorithm based on stream computing. Sci. Program. 2023 (2023)","DOI":"10.1155\/2023\/8345603"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Jeni, L.A., Cohn, J.F., De La Torre, F.: Facing imbalanced data-recommendations for the use of performance metrics. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, IEEE, (2013)","DOI":"10.1109\/ACII.2013.47"},{"key":"25_CR29","unstructured":"Nguyen, D., Fablet, R.: TrAISformer-a generative transformer for AIS trajectory prediction. arXiv preprint arXiv:2109.03958 (2021)"}],"container-title":["Lecture Notes in Computer Science","Learning and Intelligent Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75623-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T09:09:32Z","timestamp":1741684172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75623-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031756221","9783031756238"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75623-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LION","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Learning and Intelligent Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ischia Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lion2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.lion18.unina.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}