Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33010
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dc.contributor.authorZamboni, Pedroen_UK
dc.contributor.authorMarcato Junior, Joséen_UK
dc.contributor.authorde Andrade Silva, Jonathanen_UK
dc.contributor.authorMiyoshi, Gabriela Takahashien_UK
dc.contributor.authorMatsubara, Edson Takashien_UK
dc.contributor.authorNogueira, Keilleren_UK
dc.contributor.authorGonçalves, Wesley Nunesen_UK
dc.date.accessioned2021-07-30T00:04:43Z-
dc.date.available2021-07-30T00:04:43Z-
dc.date.issued2021-07en_UK
dc.identifier.other2482en_UK
dc.identifier.urihttp://hdl.handle.net/1893/33010-
dc.description.abstractrban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.en_UK
dc.language.isoenen_UK
dc.publisherMDPI AGen_UK
dc.relationZamboni P, Marcato Junior J, de Andrade Silva J, Miyoshi GT, Matsubara ET, Nogueira K & Gonçalves WN (2021) Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images. Remote Sensing, 13 (13), Art. No.: 2482. https://doi.org/10.3390/rs13132482en_UK
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectobject detectionen_UK
dc.subjectconvolutional neural networken_UK
dc.subjectremote sensingen_UK
dc.titleBenchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Imagesen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.3390/rs13132482en_UK
dc.citation.jtitleRemote Sensingen_UK
dc.citation.issn2072-4292en_UK
dc.citation.volume13en_UK
dc.citation.issue13en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderFundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sulen_UK
dc.contributor.funderBrazilian National Research Councilen_UK
dc.contributor.funderConselho Nacional de Desenvolvimento Científico e Tecnológicoen_UK
dc.contributor.funderCoordenação de Aperfeiçoamento de Pessoal de Nível Superioren_UK
dc.contributor.funderConselho Nacional de Desenvolvimento Científico e Tecnológicoen_UK
dc.citation.date25/06/2021en_UK
dc.contributor.affiliationFederal University of Mato Grosso do Sulen_UK
dc.contributor.affiliationFederal University of Mato Grosso do Sulen_UK
dc.contributor.affiliationFederal University of Mato Grosso do Sulen_UK
dc.contributor.affiliationSao Paulo State University (Universidade Estadual Paulista)en_UK
dc.contributor.affiliationFederal University of Mato Grosso do Sulen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationFederal University of Mato Grosso do Sulen_UK
dc.identifier.isiWOS:000671127300001en_UK
dc.identifier.scopusid2-s2.0-85109397264en_UK
dc.identifier.wtid1744346en_UK
dc.contributor.orcid0000-0001-5741-3621en_UK
dc.contributor.orcid0000-0002-9096-6866en_UK
dc.contributor.orcid0000-0002-8571-1383en_UK
dc.contributor.orcid0000-0002-4471-0886en_UK
dc.contributor.orcid0000-0003-3308-6384en_UK
dc.contributor.orcid0000-0002-8815-6653en_UK
dc.date.accepted2021-06-15en_UK
dcterms.dateAccepted2021-06-15en_UK
dc.date.filedepositdate2021-07-29en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorZamboni, Pedro|0000-0001-5741-3621en_UK
local.rioxx.authorMarcato Junior, José|0000-0002-9096-6866en_UK
local.rioxx.authorde Andrade Silva, Jonathan|en_UK
local.rioxx.authorMiyoshi, Gabriela Takahashi|0000-0002-8571-1383en_UK
local.rioxx.authorMatsubara, Edson Takashi|0000-0002-4471-0886en_UK
local.rioxx.authorNogueira, Keiller|0000-0003-3308-6384en_UK
local.rioxx.authorGonçalves, Wesley Nunes|0000-0002-8815-6653en_UK
local.rioxx.projectProject ID unknown|Brazilian National Research Council|en_UK
local.rioxx.freetoreaddate2021-07-29en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2021-07-29|en_UK
local.rioxx.filenameremotesensing-13-02482.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2072-4292en_UK
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