1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30630
DC FieldValueLanguage
dc.contributor.authorKalogirou, Eleftheria-
dc.contributor.authorMelillos, George-
dc.contributor.authorMakri, Despina-
dc.contributor.authorHadjimitsis, Diofantos G.-
dc.contributor.editorHou, Weilin-
dc.contributor.editorMullen, Linda J.-
dc.date.accessioned2023-10-11T11:14:31Z-
dc.date.available2023-10-11T11:14:31Z-
dc.date.issued2023-05-03-
dc.identifier.citationOcean Sensing and Monitoring XV 2023, Orlando, Florida, 3 - 4 May 2023en_US
dc.identifier.isbn9781510662025-
dc.identifier.issn0277786X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30630-
dc.description.abstractMaritime surveillance is critical for threat prevention, national security, and safety maintenance. Maritime traffic involves the worldwide navigation of millions of vessels. Satellite missions like Sentinel-1 made it possible to collect systematic data for vessel tracking. Using the Copernicus Open Access Hub service, it is now feasible to access satellite data in fully automated and near real-time mode and deliver vessel information through a web portal interface. This paper aims to detect vessels in exclusive economic zone (EEZ) of the Republic of Cyprus, Southwest of Cyprus using freely available Sentinel- 1 SAR imagery data. Comparison of results obtained using Sentinel Application Platform (SNAP) and Arc GIS Pro - Deep Learning. The preliminary results show that both tools can be used to ship detection and give satisfactory results.en_US
dc.language.isoenen_US
dc.rights© SPIEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArc GIS Proen_US
dc.subjectCyprusen_US
dc.subjectDetect ships Using Deep Learningen_US
dc.subjectMaritime safetyen_US
dc.subjectSNAPen_US
dc.titleDetecting Vessels in exclusive economic zone (EEZ) of the Republic of Cyprus using Sentinel-1en_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationERATOSTHENES Centre of Excellenceen_US
dc.subject.categoryCivil Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.identifier.doi10.1117/12.2663506en_US
dc.identifier.scopus2-s2.0-85166467827-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85166467827-
dc.relation.volume12543en_US
cut.common.academicyear2022-2023en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.languageiso639-1en-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0009-0005-0188-0200-
crisitem.author.orcid0000-0002-8292-1836-
crisitem.author.orcid0000-0002-2684-547X-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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