Academic Journal

Segmentation and sampling method for complex polyline generalization based on a generative adversarial network.

التفاصيل البيبلوغرافية
العنوان: Segmentation and sampling method for complex polyline generalization based on a generative adversarial network.
المؤلفون: Du, Jiawei, Wu, Fang, Xing, Ruixing, Gong, Xianyong, Yu, Linyi
المصدر: Geocarto International; Jul2022, Vol. 37 Issue 14, p4158-4180, 23p
مصطلحات موضوعية: GENERATIVE adversarial networks, GENERALIZATION, SOIL sampling, SAMPLING methods
مستخلص: This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline generalization using the generative adversarial network model, Pix2Pix, is developed. Third, this use case is applied experimentally for the complex generalization of coastline data from a scale of 1:50,000 to 1:250,000. Additionally, contrast experiments are conducted to compare the proposed segmentation and sampling method with object-based and traditional fixed-size methods. Experimental results show that the images generated using the proposed method are superior to the other two methods in the learning and application of complex polyline generalization. The results generalized for the developed use case are globally reasonable and suitably accurate. [ABSTRACT FROM AUTHOR]
Copyright of Geocarto International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
قاعدة البيانات: Complementary Index
ResultId 1
Header edb
Complementary Index
158597313
949
6
Academic Journal
academicJournal
949.393249511719
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edb&AN=158597313&custid=s6537998&authtype=sso
FullText Array ( [Availability] => 0 )
Items Array ( [Name] => Title [Label] => Title [Group] => Ti [Data] => Segmentation and sampling method for complex polyline generalization based on a generative adversarial network. )
Array ( [Name] => Author [Label] => Authors [Group] => Au [Data] => <searchLink fieldCode="AR" term="%22Du%2C+Jiawei%22">Du, Jiawei</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Fang%22">Wu, Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Ruixing%22">Xing, Ruixing</searchLink><br /><searchLink fieldCode="AR" term="%22Gong%2C+Xianyong%22">Gong, Xianyong</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Linyi%22">Yu, Linyi</searchLink> )
Array ( [Name] => TitleSource [Label] => Source [Group] => Src [Data] => Geocarto International; Jul2022, Vol. 37 Issue 14, p4158-4180, 23p )
Array ( [Name] => Subject [Label] => Subject Terms [Group] => Su [Data] => <searchLink fieldCode="DE" term="%22GENERATIVE+adversarial+networks%22">GENERATIVE adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22GENERALIZATION%22">GENERALIZATION</searchLink><br /><searchLink fieldCode="DE" term="%22SOIL+sampling%22">SOIL sampling</searchLink><br /><searchLink fieldCode="DE" term="%22SAMPLING+methods%22">SAMPLING methods</searchLink> )
Array ( [Name] => Abstract [Label] => Abstract [Group] => Ab [Data] => This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline generalization using the generative adversarial network model, Pix2Pix, is developed. Third, this use case is applied experimentally for the complex generalization of coastline data from a scale of 1:50,000 to 1:250,000. Additionally, contrast experiments are conducted to compare the proposed segmentation and sampling method with object-based and traditional fixed-size methods. Experimental results show that the images generated using the proposed method are superior to the other two methods in the learning and application of complex polyline generalization. The results generalized for the developed use case are globally reasonable and suitably accurate. [ABSTRACT FROM AUTHOR] )
Array ( [Name] => Abstract [Label] => [Group] => Ab [Data] => <i>Copyright of Geocarto International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) )
RecordInfo Array ( [BibEntity] => Array ( [Identifiers] => Array ( [0] => Array ( [Type] => doi [Value] => 10.1080/10106049.2021.1878288 ) ) [Languages] => Array ( [0] => Array ( [Code] => eng [Text] => English ) ) [PhysicalDescription] => Array ( [Pagination] => Array ( [PageCount] => 23 [StartPage] => 4158 ) ) [Subjects] => Array ( [0] => Array ( [SubjectFull] => GENERATIVE adversarial networks [Type] => general ) [1] => Array ( [SubjectFull] => GENERALIZATION [Type] => general ) [2] => Array ( [SubjectFull] => SOIL sampling [Type] => general ) [3] => Array ( [SubjectFull] => SAMPLING methods [Type] => general ) ) [Titles] => Array ( [0] => Array ( [TitleFull] => Segmentation and sampling method for complex polyline generalization based on a generative adversarial network. [Type] => main ) ) ) [BibRelationships] => Array ( [HasContributorRelationships] => Array ( [0] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Du, Jiawei ) ) ) [1] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Wu, Fang ) ) ) [2] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Xing, Ruixing ) ) ) [3] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Gong, Xianyong ) ) ) [4] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Yu, Linyi ) ) ) ) [IsPartOfRelationships] => Array ( [0] => Array ( [BibEntity] => Array ( [Dates] => Array ( [0] => Array ( [D] => 15 [M] => 07 [Text] => Jul2022 [Type] => published [Y] => 2022 ) ) [Identifiers] => Array ( [0] => Array ( [Type] => issn-print [Value] => 10106049 ) ) [Numbering] => Array ( [0] => Array ( [Type] => volume [Value] => 37 ) [1] => Array ( [Type] => issue [Value] => 14 ) ) [Titles] => Array ( [0] => Array ( [TitleFull] => Geocarto International [Type] => main ) ) ) ) ) ) )
IllustrationInfo