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dc.contributor.authorKoirala, Madhu
dc.contributor.authorEllingsen, Pål Gunnar
dc.contributor.authorÅdland, Roar Os
dc.date.accessioned2024-01-30T14:10:56Z
dc.date.available2024-01-30T14:10:56Z
dc.date.issued2023-10-20
dc.description.abstractIn this paper, we present a novel concept of tracking cargoes at open ports using remote sensing images and convolution neural network (CNN) to classify various dry bulk commodities. The dataset used is prepared using Sentinel-2 atmospherically corrected (Sentinel-2 L2A) images covering 12 spectral bands. There are total 4995 labeled and geo-referenced images for four different cargoes-bauxite, coal, limestone and logs. We provide benchmarks for this dataset using a CNN. The overall classification accuracy achieved was more than 90% for all cargo types. The dataset finds its applications in detecting and identifying cargoes on open ports.en_US
dc.identifier.citationKoirala M, Ellingsen PG, Ådland RO: Classification of bulk cargo types stored openly at ports using CNN. In: Yueh. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023. IEEE conference proceedings p. 6874-6877en_US
dc.identifier.cristinIDFRIDAID 2202545
dc.identifier.isbn9798350320107
dc.identifier.urihttps://hdl.handle.net/10037/32777
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.projectIDNorges forskningsråd: 326609en_US
dc.relation.urihttps://hdl.handle.net/10037/31019
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.subjectVDP::Teknologi: 500::Marin teknologi: 580en_US
dc.subjectVDP::Technology: 500::Marine technology: 580en_US
dc.subjectSkipsfartsøkonomi / Shipping Economicsen_US
dc.titleClassification of bulk cargo types stored openly at ports using CNNen_US
dc.type.versionacceptedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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