{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T19:02:58Z","timestamp":1767034978900,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,5,18]],"date-time":"2019-05-18T00:00:00Z","timestamp":1558137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,18]],"date-time":"2019-05-18T00:00:00Z","timestamp":1558137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100010669","name":"H2020 LEIT Information and Communication Technologies","doi-asserted-by":"publisher","award":["732310"],"award-info":[{"award-number":["732310"]}],"id":[{"id":"10.13039\/100010669","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AWS Cloud Credits for Research"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Geoinformatica"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s10707-019-00365-y","type":"journal-article","created":{"date-parts":[[2019,5,18]],"date-time":"2019-05-18T15:20:54Z","timestamp":1558192854000},"page":"601-622","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A comparison of supervised learning schemes for the detection of search and rescue (SAR) vessel patterns"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4998-8406","authenticated-orcid":false,"given":"Konstantinos","family":"Chatzikokolakis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios","family":"Zissis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giannis","family":"Spiliopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Tserpes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,18]]},"reference":[{"issue":"2","key":"365_CR1","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.pocean.2008.10.009","volume":"79","author":"S Bertrand","year":"2008","unstructured":"Bertrand S, D\u00edaz E, Lengaigne M (2008) Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit fishing data. Prog Oceanogr 79(2):379\u2013389. https:\/\/doi.org\/10.1016\/j.pocean.2008.10.009","journal-title":"Prog Oceanogr"},{"issue":"1","key":"365_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"365_CR3","doi-asserted-by":"crossref","unstructured":"Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. Proceedings of the 23rd international conference on machine learning (New York, NY, USA, 2006), pp 161\u2013168","DOI":"10.1145\/1143844.1143865"},{"key":"365_CR4","unstructured":"Chatzikokolakis K, Zissis D, Spiliopoulos G, Tserpes K (2018) Mining vessel trajectory data for patterns of search and rescue. EDBT\/ICDT workshops 2018, pp 117\u2013124"},{"issue":"4","key":"365_CR5","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/TPDS.2016.2603511","volume":"28","author":"J Chen","year":"2017","unstructured":"Chen J, Li K, Tang Z, Bilal K, Yu S, Weng C, Li K (2017) A parallel random Forest algorithm for big data in a spark cloud computing environment. IEEE Trans Parallel Distrib Syst 28(4):919\u2013933. https:\/\/doi.org\/10.1109\/TPDS.2016.2603511","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"365_CR6","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining (New York, NY, USA, 2016), pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"365_CR7","unstructured":"Ester M, Kriegel H-P, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the second international conference on knowledge discovery and data mining (Portland, Oregon, 1996), pp 226\u2013231"},{"key":"365_CR8","unstructured":"Falcon R, Abielmona R, Blasch E (2014) Behavioral learning of vessel types with fuzzy-rough decision trees. 17th International Conference on Information Fusion (FUSION) (Jul. 2014), pp 1\u20138"},{"issue":"5","key":"365_CR9","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189\u20131232","journal-title":"Ann Stat"},{"key":"365_CR10","unstructured":"Risk analysis for 2017 (2017) [ebook] Frontex and European border and coast guard agency. Available at: https:\/\/frontex.europa.eu\/assets\/Publications\/Risk_Analysis\/Annual_Risk_Analysis_2017.pdf. Accessed 4 March 2019"},{"key":"365_CR11","unstructured":"Galdorisi G, Goshorn R (2006) Maritime domain awareness: the key to maritime security operational challenges and technical solutions. Ft. Belvoir: Defense Technical Information Center, 2006. http:\/\/handle.dtic.mil\/100.2\/ADA457569"},{"issue":"Sep. 2017","key":"365_CR12","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.bdr.2017.07.003","volume":"9","author":"R Genuer","year":"2017","unstructured":"Genuer R, Poggi J-M, Tuleau-Malot C, Villa-Vialaneix N (2017) Random forests for big data. Big Data Research 9(Sep. 2017):28\u201346. https:\/\/doi.org\/10.1016\/j.bdr.2017.07.003","journal-title":"Big Data Research"},{"key":"365_CR13","doi-asserted-by":"publisher","unstructured":"Huang H, Hong F, Liu J, Liu C, Feng Y, Guo Z (2018) FVID: fishing vessel type identification based on VMS trajectories. J Ocean Univ China. https:\/\/doi.org\/10.1007\/s11802-018-3717-1","DOI":"10.1007\/s11802-018-3717-1"},{"key":"365_CR14","unstructured":"Mixed Migration Flows in the Mediterranean and Beyond (2017) [ebook] International Organization for Migration. Available at: http:\/\/migration.iom.int\/docs\/2016_Flows_to_Europe_Overview.pdf. Accessed 4 March 2019"},{"key":"365_CR15","doi-asserted-by":"crossref","unstructured":"Jiang X, Silver DL, Hu B, Souza EN, Matwin S (2016) Fishing activity detection from AIS data using autoencoders. Proceedings of the 29th Canadian conference on artificial intelligence on advances in artificial intelligence - volume 9673 (New York, NY, USA, 2016), pp 33\u201339","DOI":"10.1007\/978-3-319-34111-8_4"},{"issue":"4","key":"365_CR16","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1016\/j.ecolmodel.2010.08.039","volume":"222","author":"R Joo","year":"2011","unstructured":"Joo R, Bertrand S, Chaigneau A, \u00d1iquen M (2011) Optimization of an artificial neural network for identifying fishing set positions from VMS data: an example from the Peruvian anchovy purse seine fishery. Ecol Model 222(4):1048\u20131059. https:\/\/doi.org\/10.1016\/j.ecolmodel.2010.08.039","journal-title":"Ecol Model"},{"key":"365_CR17","doi-asserted-by":"publisher","unstructured":"Lee J-G, Han J, Li X, Gonzalez H (2008) TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc VLDB Endow 1(1):1081\u20131094. https:\/\/doi.org\/10.14778\/1453856.1453972","DOI":"10.14778\/1453856.1453972"},{"issue":"Feb. 2013","key":"365_CR18","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1016\/j.snb.2012.11.071","volume":"177","author":"M Liu","year":"2013","unstructured":"Liu M, Wang M, Wang J, Li D (2013) Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: application to the recognition of orange beverage and Chinese vinegar. Sensors Actuators B Chem 177(Feb. 2013):970\u2013980. https:\/\/doi.org\/10.1016\/j.snb.2012.11.071","journal-title":"Sensors Actuators B Chem"},{"key":"365_CR19","doi-asserted-by":"publisher","unstructured":"Marzuki MI, Gaspar P, Garello R, Kerbaol V, Fablet R (2017) Fishing gear identification from vessel-monitoring-system-based fishing vessel trajectories. IEEE J Ocean Eng 689\u2013699.\u00a0https:\/\/doi.org\/10.1109\/JOE.2017.2723278","DOI":"10.1109\/JOE.2017.2723278"},{"key":"365_CR20","unstructured":"Mazzarella F, Vespe M, Damalas D, Osio G (2014) Discovering vessel activities at sea using AIS data: mapping of fishing footprints. 17th International conference on information fusion (FUSION) (Jul. 2014), pp 1\u20137"},{"issue":"6","key":"365_CR21","doi-asserted-by":"publisher","first-page":"e0130746","DOI":"10.1371\/journal.pone.0130746","volume":"10","author":"F Natale","year":"2015","unstructured":"Natale F, Gibin M, Alessandrini A, Vespe M, Paulrud A (2015) Mapping fishing effort through AIS data. PLoS One 10(6):e0130746. https:\/\/doi.org\/10.1371\/journal.pone.0130746","journal-title":"PLoS One"},{"issue":"2","key":"365_CR22","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1139\/F08-208","volume":"66","author":"M Palmer","year":"2009","unstructured":"Palmer M, Quetglas A, Guijarro B, Moranta J, Ordines F, Massut\u00ed E (2009) Performance of artificial neural networks and discriminant analysis in predicting fishing tactics from multispecific fisheries. Can J Fish Aquat Sci 66(2):224\u2013237. https:\/\/doi.org\/10.1139\/F08-208","journal-title":"Can J Fish Aquat Sci"},{"issue":"3","key":"365_CR23","doi-asserted-by":"publisher","first-page":"25","DOI":"10.2478\/v10047-012-0015-3","volume":"49","author":"J Po\u013cevskis","year":"2012","unstructured":"Po\u013cevskis J, Krasti\u0146\u0161 M, Kor\u0101ts G, Skorodumovs A, Trok\u0161s J (2012) Methods for processing and interpretation of AIS signals corrupted by noise and packet collisions. Latv J Phys Tech Sci 49(3):25\u201331. https:\/\/doi.org\/10.2478\/v10047-012-0015-3","journal-title":"Latv J Phys Tech Sci"},{"key":"365_CR24","doi-asserted-by":"crossref","unstructured":"Rocha JAMR, Times VC, Oliveira G, Alvares LO, Bogorny V (2010) DB-SMoT: a direction-based spatio-temporal clustering method. 2010 5th IEEE international conference intelligent systems (Jul. 2010), pp 114\u2013119","DOI":"10.1109\/IS.2010.5548396"},{"issue":"1","key":"365_CR25","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.fishres.2011.06.011","volume":"111","author":"T Russo","year":"2011","unstructured":"Russo T, Parisi A, Prorgi M, Boccoli F, Cignini I, Tordoni M, Cataudella S (2011) When behaviour reveals activity: assigning fishing effort to m\u00e9tiers based on VMS data using artificial neural networks. Fish Res 111(1):53\u201364. https:\/\/doi.org\/10.1016\/j.fishres.2011.06.011","journal-title":"Fish Res"},{"issue":"7","key":"365_CR26","doi-asserted-by":"publisher","first-page":"e0158248","DOI":"10.1371\/journal.pone.0158248","volume":"11","author":"EN de Souza","year":"2016","unstructured":"de Souza EN, Boerder K, Matwin S, Worm B (2016) Improving fishing pattern detection from satellite AIS using data mining and machine learning. PLoS One 11(7):e0158248. https:\/\/doi.org\/10.1371\/journal.pone.0158248","journal-title":"PLoS One"},{"key":"365_CR27","doi-asserted-by":"crossref","unstructured":"Spiliopoulos G, Zissis D, Chatzikokolakis K (2017) A big data driven approach to extracting global trade patterns. In International workshop on mobility analytics for Spatio-temporal and social data (Sep. 2017), pp 109\u2013121.","DOI":"10.1007\/978-3-319-73521-4_7"},{"issue":"1","key":"365_CR28","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/1471-2105-8-25","volume":"8","author":"C Strobl","year":"2007","unstructured":"Strobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinf 8(1):25. https:\/\/doi.org\/10.1186\/1471-2105-8-25","journal-title":"BMC Bioinf"},{"issue":"4","key":"365_CR29","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1037\/a0016973","volume":"14","author":"C Strobl","year":"2009","unstructured":"Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychol Methods 14(4):323\u2013348. https:\/\/doi.org\/10.1037\/a0016973","journal-title":"Psychol Methods"},{"key":"365_CR30","unstructured":"Data2.unhcr.org. (2019)\u00a0Situation Mediterranean Situation. [online] Available at: https:\/\/data2.unhcr.org\/en\/situations\/mediterranean. Accessed 4 March 2019"},{"key":"365_CR31","doi-asserted-by":"crossref","unstructured":"Varlamis I, Tserpes K, Sardianos C (2018) Detecting search and rescue Missions from AIS data. 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW) (Paris, Apr 2018), pp 60\u201365","DOI":"10.1109\/ICDEW.2018.00017"},{"issue":"18","key":"365_CR32","doi-asserted-by":"publisher","first-page":"13426","DOI":"10.1016\/j.eswa.2012.05.060","volume":"39","author":"GKD de Vries","year":"2012","unstructured":"de Vries GKD, van Someren M (2012) Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Syst Appl 39(18):13426\u201313439. https:\/\/doi.org\/10.1016\/j.eswa.2012.05.060","journal-title":"Expert Syst Appl"},{"key":"365_CR33","doi-asserted-by":"crossref","unstructured":"Yang M, Zou Y, Fang L (2012) Collision and detection performance with three overlap signal collisions in space-based AIS reception. 2012 IEEE 11th international conference on trust, security and privacy in computing and communications (Jun. 2012), pp 1641\u20131648","DOI":"10.1109\/TrustCom.2012.109"},{"key":"365_CR34","doi-asserted-by":"crossref","unstructured":"Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw Gps data for geographic applications on the web. Proceedings of the 17th international conference on world wide web (New York, NY, USA, 2008), pp 247\u2013256","DOI":"10.1145\/1367497.1367532"},{"key":"365_CR35","unstructured":"Recommendation ITU-R M.1371-5: Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band (2014) [ebook] International Telecommunication Union - Radiocommunication sector. Available at: https:\/\/www.itu.int\/dms_pubrec\/itu-r\/rec\/m\/R-REC-M.1371-5-201402-I!!PDF-E.pdf. Accessed 4 March 2019"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-019-00365-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-019-00365-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-019-00365-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:11:01Z","timestamp":1635729061000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-019-00365-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,18]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["365"],"URL":"https:\/\/doi.org\/10.1007\/s10707-019-00365-y","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"type":"print","value":"1384-6175"},{"type":"electronic","value":"1573-7624"}],"subject":[],"published":{"date-parts":[[2019,5,18]]},"assertion":[{"value":"25 July 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}