There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 1153 | - |
dc.citation.number | 4-5 | - |
dc.citation.startPage | 1145 | - |
dc.citation.title | Korean Journal of Remote Sensing | - |
dc.citation.volume | 39 | - |
dc.contributor.author | Lee, Yeonsu | - |
dc.contributor.author | Son, Bokyung | - |
dc.contributor.author | Im, Jungho | - |
dc.date.accessioned | 2024-02-20T17:05:11Z | - |
dc.date.available | 2024-02-20T17:05:11Z | - |
dc.date.created | 2024-02-15 | - |
dc.date.issued | 2023-10 | - |
dc.description.abstract | Urban trees play an important role in absorbing carbon dioxide from the atmosphere, improving air quality, mitigating the urban heat island effect, and providing ecosystem services. To effectively manage and conserve urban trees, accurate spatial information on their location, condition, species, and population is needed. In this study, we propose an algorithm that uses a high-resolution urban tree cover map constructed from deep learning approach to separate trees from the urban land surface and accurately detect tree locations through local maximum filtering. Instead of using a uniform filter size, we improved the tree detection performance by selecting the appropriate filter size according to the tree height in consideration of various urban growth environments. The research output, the location and height of individual trees in human settlement over Suwon, will serve as a basis for sustainable management of urban ecosystems and carbon reduction measures. Copyright © 2023 by The Korean Society of Remote Sensing. | - |
dc.identifier.bibliographicCitation | Korean Journal of Remote Sensing, v.39, no.4-5, pp.1145 - 1153 | - |
dc.identifier.doi | 10.7780/kjrs.2023.39.5.4.8 | - |
dc.identifier.issn | 1225-6161 | - |
dc.identifier.scopusid | 2-s2.0-85177564206 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/81436 | - |
dc.language | 한국어 | - |
dc.publisher | 대한원격탐사학회 | - |
dc.title | Detection of Individual Trees in Human Settlement Using Airborne LiDAR Data and Deep Learning-Based Urban Green Space Map | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Human settlement | - |
dc.subject.keywordAuthor | Individual tree detection | - |
dc.subject.keywordAuthor | Urban ecosystem | - |
dc.subject.keywordAuthor | Urban trees | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.