Benchmarking anchor-based and anchor-free state-of-the-art deep learning methods for individual tree detection in rgb high-resolution images

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Autor(es): dc.contributorFederal University of Mato Grosso do Sul-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Stirling-
Autor(es): dc.creatorZamboni, Pedro-
Autor(es): dc.creatorJunior, José Marcato-
Autor(es): dc.creatorSilva, Jonathan de Andrade-
Autor(es): dc.creatorMiyoshi, Gabriela Takahashi-
Autor(es): dc.creatorMatsubara, Edson Takashi-
Autor(es): dc.creatorNogueira, Keiller-
Autor(es): dc.creatorGonçalves, Wesley Nunes-
Data de aceite: dc.date.accessioned2025-08-21T19:25:12Z-
Data de disponibilização: dc.date.available2025-08-21T19:25:12Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs13132482-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229100-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229100-
Descrição: dc.descriptionUrban 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFaculty of Engineering Architecture and Urbanism and Geography Federal University of Mato Grosso do Sul-
Descrição: dc.descriptionFaculty of Computer Science Federal University of Mato Grosso do Sul-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP)-
Descrição: dc.descriptionComputing Science and Mathematics Division Faculty of Natural Sciences University of Stirling-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP)-
Descrição: dc.descriptionCNPq: 303559/2019-5-
Descrição: dc.descriptionCNPq: 304052/2019-1-
Descrição: dc.descriptionCNPq: 433783/2018-4-
Descrição: dc.descriptionCAPES: 88881.311850/2018-01-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvolutional neural network-
Palavras-chave: dc.subjectObject detection-
Palavras-chave: dc.subjectRemote sensing-
Título: dc.titleBenchmarking anchor-based and anchor-free state-of-the-art deep learning methods for individual tree detection in rgb high-resolution images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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