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dc.contributor.authorSusaki, Junichien
dc.contributor.authorKubota, Seiyaen
dc.contributor.alternative須崎, 純一ja
dc.date.accessioned2017-07-04T05:21:30Z-
dc.date.available2017-07-04T05:21:30Z-
dc.date.issued2017-02-27-
dc.identifier.issn2072-4292-
dc.identifier.urihttp://hdl.handle.net/2433/226317-
dc.description.abstractIn this paper, we propose a method for using mobile laser-scanning data to estimate the green space ratio (GSR), a landscape index that represents the proportion of green area to the whole-view area. The proposed method first classifies and segments vegetation using voxel-based and shape-based approaches. Vertical planar-surface objects are excluded, and randomly distributed objects are extracted as vegetation via multi-spatial-scale analysis. Then, the method generates a map representing occlusion by vegetation, and estimates GSR at an arbitrary location. We applied the method to a data set collected in a residential area in Kyoto, Japan. We compared the results with the ground truth data and obtained a root mean squared error of approximately 4.1%. Although some non-vegetation with rough surfaces was falsely extracted as vegetation, our method seems to estimate GSR to an acceptable accuracy.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherMDPI AGen
dc.rights© 2017 by the authors.en
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).en
dc.subjectGreen space ratioen
dc.subjectMobile scanning dataen
dc.subjectMulti-scale analysisen
dc.subjectUrban landscapeen
dc.titleAutomatic assessment of green space ratio in urban areas from mobile scanning dataen
dc.typejournal article-
dc.type.niitypeJournal Article-
dc.identifier.jtitleRemote Sensingen
dc.identifier.volume9-
dc.identifier.issue3-
dc.relation.doi10.3390/rs9030215-
dc.textversionpublisher-
dc.identifier.artnum215-
dcterms.accessRightsopen access-
出現コレクション:学術雑誌掲載論文等

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