Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29159
DC FieldValueLanguage
dc.contributor.authorTopouzelis, Konstantinos-
dc.contributor.authorPapakonstantinou, Apostolos-
dc.contributor.authorBatsaris, Marios-
dc.contributor.authorSpondylidis, Spyros-
dc.date.accessioned2023-05-09T06:17:17Z-
dc.date.available2023-05-09T06:17:17Z-
dc.date.issued2021-01-01-
dc.identifier.citation2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 11-16 July 2021en_US
dc.identifier.isbn9781665403696-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/29159-
dc.description.abstractThe detection of the coastal and Marine Litter (ML) using UAS data in combination with machine learning methods is an important step towards an automated process of detecting and mapping ML concentrations. The Visual Geometry Group-19 (VGG19) CNN architecture is used to classify UAS image tiles in two classes; litter and no litter. Testing the geographical transferability of our method to an unseen dataset, we found that the VVG19 CNN obtained an overall accuracy of 83.67 % and an F-score of 81.63%. The produced ML density maps can be used as a decision-making support tool. The integration and visualization of the marine litter and density information facilitate decision-making by all related to the problem stakeholders and decision-makers.en_US
dc.language.isoenen_US
dc.subjectMarine litteren_US
dc.subjectAIen_US
dc.subjectDroneen_US
dc.subjectCoastal areaen_US
dc.titleINTEGRATED MONITORING SYSTEM FOR BEACH LITTER PREPAREDNESS AND RESPONSEen_US
dc.typeConference Papersen_US
dc.collaborationUniversity of the Aegeanen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryGreeceen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceInternational Geoscience and Remote Sensing Symposium (IGARSS)en_US
dc.identifier.doi10.1109/IGARSS47720.2021.9553443en_US
dc.identifier.scopus2-s2.0-85126041829en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85126041829en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyear2020-2021en_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-6464-2008-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

Page view(s) 5

171
Last Week
8
Last month
2
checked on Feb 16, 2025

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.