{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:34:22Z","timestamp":1773513262825,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031245206","type":"print"},{"value":"9783031245213","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-24521-3_9","type":"book-chapter","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T12:12:28Z","timestamp":1673957548000},"page":"113-132","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AIS Data Driven CNN-BiGRU Model for Ship Target Classification"],"prefix":"10.1007","author":[{"given":"Yujun","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jian","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Li","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Kexin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zongming","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"issue":"3","key":"9_CR1","first-page":"53","volume":"35","author":"JH Chen","year":"2012","unstructured":"Chen, J.H., Lu, F., Peng, G.J.: The progress of research in maritime vessel trajectory analysis. China Navig. 35(3), 53\u201357 (2012)","journal-title":"China Navig."},{"issue":"4","key":"9_CR2","first-page":"15","volume":"41","author":"Z Deng","year":"2021","unstructured":"Deng, Z., Li, Z.F., Duan, W., Li, Z.K.: Spatial-temporal distribution pattern of ship activity in maritime silk road based on AIS data. Econ. Geogr. 41(4), 15\u201322 (2021)","journal-title":"Econ. Geogr."},{"issue":"5","key":"9_CR3","first-page":"23","volume":"6","author":"Y Liang","year":"2020","unstructured":"Liang, Y., Zhang, H.: Ship track prediction based on AIS data and PSO optimized LSTM network. Int. Core J. Eng. 6(5), 23\u201333 (2020)","journal-title":"Int. Core J. Eng."},{"issue":"2","key":"9_CR4","first-page":"135","volume":"39","author":"Y Zhou","year":"2017","unstructured":"Zhou, Y.: Research on collaborative IoT big data processing system in maritime transportation. Naval Sci. Technol. 39(2), 135\u2013137 (2017)","journal-title":"Naval Sci. Technol."},{"issue":"2","key":"9_CR5","first-page":"11","volume":"3","author":"L Zhou","year":"2015","unstructured":"Zhou, L., Zhao, X.S., Wang, J.G., Tang, J.B., Bai, Y.Y.: Research on BDS application in maritime intelligent transport security system. J. Navig. Position. 3(2), 11\u201315 (2015)","journal-title":"J. Navig. Position."},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Wang, B., Wang, Y., Qin, K.: Detecting transportation modes based on LightGBM classifier from GPS trajectory data. In: 2018 26th International Conference on Geoinformatics, pp. 32\u201341. ACM (2018)","DOI":"10.1109\/GEOINFORMATICS.2018.8557149"},{"issue":"12","key":"9_CR7","first-page":"3527","volume":"33","author":"Z Li","year":"2016","unstructured":"Li, Z., Sun, J., Ni, X.Y.: Travel mode recognition based on smart phone big data. Appl. Res. Comput. 33(12), 3527\u20133529 (2016)","journal-title":"Appl. Res. Comput."},{"issue":"2","key":"9_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3390\/ijgi6020057","volume":"6","author":"ZB Xiao","year":"2017","unstructured":"Xiao, Z.B., Wang, Y., Fu, K., Wu, F.: Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS Int. J. Geo Inf. 6(2), 57 (2017)","journal-title":"ISPRS Int. J. Geo Inf."},{"issue":"10","key":"9_CR9","first-page":"68","volume":"126","author":"SS Xiong","year":"2018","unstructured":"Xiong, S.S.: Identifying transportation mode based on improved LightGBM algorithm. Comput. Modernization 126(10), 68\u201373 (2018)","journal-title":"Comput. Modernization"},{"issue":"2","key":"9_CR10","first-page":"81","volume":"12","author":"QL Zheng","year":"2016","unstructured":"Zheng, Q.L., Fan, W., Zhang, S.M., Zhang, H., Wang, X.X.: Identification of fishing type from VMS data based on artificial neural network. S. China Fish. Sci. 12(2), 81\u201387 (2016)","journal-title":"S. China Fish. Sci."},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1017\/S0373463317000546","volume":"1","author":"K Sheng","year":"2018","unstructured":"Sheng, K., Liu, Z., Zhou, D.C., He, A.L., Feng, C.X.: Research on ship classification based on trajectory features. J. Navig. 1, 100\u2013116 (2018)","journal-title":"J. Navig."},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Kraus, P., Mohrdieck, C., Schwenker, F.: Ship classification based on trajectory data with machine-learning methods. In: 2018 19th International Radar Symposium (IRS), pp. 1\u201310. IEEE (2018)","DOI":"10.23919\/IRS.2018.8448028"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Ginolhac, R., Barbaresco, F., Schneider, J.Y., Pannier, J.M., Savary, S.: Coastal radar target recognition based on kinematic data (AIS) with machine learning. In: 2019 International Radar Conference (RADAR), pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/RADAR41533.2019.171262"},{"issue":"12","key":"9_CR14","first-page":"1865","volume":"45","author":"S Gao","year":"2020","unstructured":"Gao, S.: A review of recent researches and reflections on geospatial artificial intelligence. Geomat. Inf. Sci. Wuhan Univ. 45(12), 1865\u20131874 (2020)","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Ljunggren, H.: Using deep learning for classifying ship trajectories. In: 2018 International Conference on Information Fusion, pp. 2158\u20132164. IEEE (2018)","DOI":"10.23919\/ICIF.2018.8455776"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Bakkegaard, S., Blixenkrone, M.J., Larsen, J.J., Jochumsen, L.: Target classification using kinematic data and a recurrent neural network. In: 2018 19th International Radar Symposium (IRS), pp. 1\u201310. IEEE (2018)","DOI":"10.23919\/IRS.2018.8448118"},{"issue":"9","key":"9_CR17","first-page":"175","volume":"47","author":"TT Cui","year":"2020","unstructured":"Cui, T.T., Wang, G.L., Gao, J.: Ship trajectory classification method based on 1DCNN-LSTM. Comput. Sci. 47(9), 175\u2013184 (2020)","journal-title":"Comput. Sci."},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Xie, X., Sun, G.Z.: Driving with knowledge from the physical word. In: Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 316\u2013324. ACM (2011)","DOI":"10.1145\/2020408.2020462"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw GPS data for geographic applications on the web. In: 17th International Conference on World Wide Web, pp. 247\u2013256. ACM (2008)","DOI":"10.1145\/1367497.1367532"},{"key":"9_CR20","doi-asserted-by":"publisher","first-page":"173918","DOI":"10.1109\/ACCESS.2020.3026110","volume":"8","author":"SC Lu","year":"2018","unstructured":"Lu, S.C., Xia, Y.: Dual supervised autoencoder based trajectory classification using enhanced spatio-temporal information. IEEE Access 8, 173918\u2013173932 (2018)","journal-title":"IEEE Access"},{"key":"9_CR21","first-page":"75","volume":"6","author":"GN Xiao","year":"2017","unstructured":"Xiao, G.N., Juan, Z.C., Zhang, C.Q.: Travel mode detection based on GPS track data and Bayesian network. Stat. Decis. 6, 75\u201379 (2017)","journal-title":"Stat. Decis."},{"issue":"2","key":"9_CR22","first-page":"67","volume":"30","author":"GJ Liu","year":"2018","unstructured":"Liu, G.J., Yang, J.F.: The classification method of traffic trajectory pattern based on deep learning and permutation entropy. J. North China Univ. Technol. 30(2), 67\u201373 (2018)","journal-title":"J. North China Univ. Technol."},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.jtrangeo.2019.06.001","volume":"78","author":"J Broach","year":"2019","unstructured":"Broach, J., Dill, J., Mcneil, N.W.: Travel mode imputation using GPS and accelerometer data from a multi-day travel survey. J. Transp. Geogr. 78, 194\u2013204 (2019)","journal-title":"J. Transp. Geogr."},{"key":"9_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.knosys.2017.02.016","volume":"123","author":"GJ Liu","year":"2017","unstructured":"Liu, G.J., Yin, Z.Z., Jia, Y.J., Xie, Y.L.: Passenger flow estimation based on convolutional neural network in public transportation system. Knowl.-Based Syst. 123, 102\u2013115 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"9_CR25","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing System, vol. 25, pp. 1097\u20131105 (2012)."},{"key":"9_CR26","unstructured":"Chung, J. Y., Gulcehre, C., Cho, K.H., Bengio, Y.S.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: 2014 Deep Learning and Representation Learning Workshop, pp. 1\u20139. Springer (2014)"},{"issue":"8","key":"9_CR27","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"9_CR28","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"issue":"6","key":"9_CR29","first-page":"916","volume":"13","author":"GH Zhang","year":"2019","unstructured":"Zhang, G.H., Liu, B.: Research on time series classification using CNN and bidirectional GRU. J. Front. Comput. Sci. Technol. 13(6), 916\u2013927 (2019)","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"9_CR30","doi-asserted-by":"publisher","first-page":"143025","DOI":"10.1109\/ACCESS.2019.2941280","volume":"7","author":"GW Dai","year":"2019","unstructured":"Dai, G.W., Ma, C.X., Xu, X.C.: Short-term traffic flow prediction method for urban road sections based on space-time analysis and GRU. IEEE Access 7, 143025\u2013143035 (2019)","journal-title":"IEEE Access"},{"key":"9_CR31","doi-asserted-by":"publisher","first-page":"70463","DOI":"10.1109\/ACCESS.2018.2878799","volume":"6","author":"JD Zhao","year":"2018","unstructured":"Zhao, J.D., Gao, Y., Qu, Y.C., Yin, H.D., Liu, Y.M., Sun, H.J.: Travel time prediction: based on gated recurrent unit method and data fusion. IEEE Access 6, 70463\u201370472 (2018)","journal-title":"IEEE Access"},{"key":"9_CR32","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: The 3rd International Conference for Learning Representations, pp. 1\u201315. Springer (2015)"},{"issue":"1","key":"9_CR33","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.trc.2017.11.021","volume":"86","author":"S Dabiri","year":"2018","unstructured":"Dabiri, S., Heaslip, K.: Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C Emerg. Technol. 86(1), 360\u2013371 (2018)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"9_CR34","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 1\u201314. Springer (2015)"},{"issue":"1","key":"9_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Spatial Data and Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24521-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:13:40Z","timestamp":1680653620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24521-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031245206","9783031245213"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24521-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"18 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SpatialDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Spatial Data and Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spatialdi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/spatialdi2022.cug.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}