Customized Atrous Spatial Pyramid Pooling with Joint Convolutions for Urban Tree Segmentation

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorJodas, Danilo Samuel-
Autor(es): dc.creatorVelasco, Giuliana Del Nero-
Autor(es): dc.creatorBrazolin, Sergio-
Autor(es): dc.creatorde Lima, Reinaldo Araujo-
Autor(es): dc.creatorPassos, Leandro Aparecido-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T16:55:20Z-
Data de disponibilização: dc.date.available2025-08-21T16:55:20Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0013090400003912-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306436-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306436-
Descrição: dc.descriptionUrban trees provide several benefits to the cities, including local climatic regulation and better life quality. Assessing the tree conditions is essential to gather important insights related to its biomechanics and the possible risk of falling. The common strategy is ruled by fieldwork campaigns to collect the tree’s physical measures like height, the trunk’s diameter, and canopy metrics for a first-glance assessment and further prediction of the possible risk to the city’s infrastructure. The canopy and trunk of the tree play an important role in the resistance analysis when exposed to severe windstorm events. However, fieldwork analysis is laborious and time-expensive because of the massive number of trees. Therefore, strategies based on computational analysis are highly demanded to promote a rapid assessment of tree conditions. This paper presents a deep learning-based approach for semantic segmentation of the trunk and canopy of trees in images acquired from the street-view perspective. The proposed strategy combines convolutional modules, spatial pyramid pooling, and attention mechanism into a U-Net-based architecture to improve the prediction capacity. Experiments performed over two image datasets showed the proposed model attained competitive results compared to previous works employing large-sized semantic segmentation models.-
Descrição: dc.descriptionAir Force Office of Scientific Research-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionOffice of Naval Research-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences-
Descrição: dc.descriptionInstitute for Technological Research University of São Paulo-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences-
Descrição: dc.descriptionFAPESP: #2013/07375-0-
Descrição: dc.descriptionFAPESP: #2014/12236-1-
Descrição: dc.descriptionFAPESP: #2019/07665-4-
Descrição: dc.descriptionFAPESP: #2019/18287-0-
Descrição: dc.descriptionFAPESP: #2023/10823-6-
Descrição: dc.descriptionFAPESP: #2023/14427-8-
Descrição: dc.descriptionCNPq: 2023/00466-1-
Descrição: dc.descriptionCNPq: 308529/2021-9-
Descrição: dc.descriptionCNPq: 400756/2024-2-
Descrição: dc.descriptionOffice of Naval Research: N62909-24-1-2012-
Formato: dc.format267-274-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAtrous Spatial Pyramid Pooling-
Palavras-chave: dc.subjectCanopy Segmentation-
Palavras-chave: dc.subjectSemantic Segmentation-
Palavras-chave: dc.subjectTrunk Segmentation-
Palavras-chave: dc.subjectUrban Tree Monitoring-
Título: dc.titleCustomized Atrous Spatial Pyramid Pooling with Joint Convolutions for Urban Tree Segmentation-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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