Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 안용한 | - |
dc.date.accessioned | 2023-01-04T02:11:28Z | - |
dc.date.available | 2023-01-04T02:11:28Z | - |
dc.date.issued | 2022-11 | - |
dc.identifier.citation | APPLIED SCIENCES-BASEL, v. 12, NO. 22, article no. 11428, | en_US |
dc.identifier.issn | 2076-3417 | en_US |
dc.identifier.uri | https://www.mdpi.com/2076-3417/12/22/11428 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/178719 | - |
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.description.sponsorship | This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22CTAP-C16390302) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20202020800030, Development of Smart Hybrid Envelope Systems for Zero Energy Buildings through Holistic Performance Test and Evaluation Methods and Fields Verifications). | - |
dc.language | en | en_US |
dc.publisher | MDPI | en_US |
dc.subject | building coverage ratio | - |
dc.subject | deep learning | - |
dc.subject | urban management | - |
dc.subject | urban density | - |
dc.subject | mask R-CNN | - |
dc.title | Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas | en_US |
dc.type | Article | en_US |
dc.relation.no | 22 | - |
dc.relation.volume | 12 | - |
dc.identifier.doi | 10.3390/app122211428 | en_US |
dc.relation.journal | APPLIED SCIENCES-BASEL | - |
dc.contributor.googleauthor | Le, Quang Hoai | - |
dc.contributor.googleauthor | Shin, Hyunkyu | - |
dc.contributor.googleauthor | Kwon, Nahyun | - |
dc.contributor.googleauthor | Ho, Jongnam | - |
dc.contributor.googleauthor | Ahn, Yonghan | - |
dc.sector.campus | E | - |
dc.sector.daehak | 공학대학 | - |
dc.sector.department | 건축공학전공 | - |
dc.identifier.pid | yhahn | - |
dc.identifier.article | 11428 | - |
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