Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Le, Quang Hoai | - |
dc.contributor.author | Shin, Hyunkyu | - |
dc.contributor.author | Kwon, Nahyun | - |
dc.contributor.author | Ho, Jongnam | - |
dc.contributor.author | Ahn, Yonghan | - |
dc.date.accessioned | 2022-12-20T04:36:36Z | - |
dc.date.available | 2022-12-20T04:36:36Z | - |
dc.date.issued | 2022-11 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111193 | - |
dc.description.abstract | Urban parameters, such as building density and the building coverage ratio (BCR), play a crucial role in urban analysis and measurement. Although several approaches have been proposed for BCR estimations, a quick and effective tool is still required due to the limitations of statistical-based and manual mapping methods. Since a building footprint is crucial for the BCR calculation, we hypothesize that Deep Learning (DL) models can aid in the BCR computation, due to their proven automatic building footprint extraction capability. Thus, this study applies the DL framework in the ArcGIS software to the BCR calculation task and evaluates its efficiency for a new industrial district in South Korea. Although the accuracy achieved was limited due to poor-quality input data and issues with the training process, the result indicated that the DL-based approach is applicable for BCR measuring, which is a step toward suggesting an implication of this method. Overall, the potential utility of this proposed approach for the BCR measurement promises to be considerable. | - |
dc.format.extent | 16 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/app122211428 | - |
dc.identifier.scopusid | 2-s2.0-85142518219 | - |
dc.identifier.wosid | 000887078200001 | - |
dc.identifier.bibliographicCitation | Applied Sciences-basel, v.12, no.22, pp 1 - 16 | - |
dc.citation.title | Applied Sciences-basel | - |
dc.citation.volume | 12 | - |
dc.citation.number | 22 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 16 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordPlus | REGION | - |
dc.subject.keywordPlus | REMOTE | - |
dc.subject.keywordAuthor | building coverage ratio | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | urban management | - |
dc.subject.keywordAuthor | urban density | - |
dc.subject.keywordAuthor | mask R-CNN | - |
dc.identifier.url | https://www.mdpi.com/2076-3417/12/22/11428 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.