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dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorMARRAFON, G.
dc.contributor.authorLEMOS, E. L. de
dc.contributor.authorFURUYA, M. T. G.
dc.contributor.authorGONÇALVES, R. D. S.
dc.contributor.authorGONÇALVES, W. N.
dc.contributor.authorMARCATO JUNIOR, J.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorLIESENBERG, V.
dc.contributor.authorOSCO, L. P.
dc.contributor.authorRAMOS, A. P. M.
dc.date.accessioned2026-04-02T12:48:26Z-
dc.date.available2026-04-02T12:48:26Z-
dc.date.created2026-04-02
dc.date.issued2026
dc.identifier.citationUrban Science, v. 10, n. 4, 192, Apr. 2026.
dc.identifier.issn2413-8851
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186025-
dc.descriptionMapping urban tree species supports green infrastructure planning. An essential issue refers to the monitoring of exotic species that may become invasive. Street-level imagery provides a complementary perspective to aerial images for species identification that are difficult to distinguish from above. In this context, our study aimed to evaluate deep learning-based object detection and image segmentation approaches to identify a potentially invasive tree species known as Leucaena leucocephala in an urban environment in Brazil, using 422 street-level images acquired from Google Street View (SV) and mobile phones (MPs). Object detection models (YOLOv8 and DETR) and a foundation segmentation model (SAM, zero-shot) were applied to assess how deep learning paradigms perform under heterogeneous urban imaging conditions. YOLOv8 achieved detection performance with mAP50 above 0.83 and recall up to 0.76. DETR showed domain sensitivity, with mAP50 of 0.45 in SV images and 0.84 in MP imagery. For segmentation, SAM zero-shot achieved 0.92 accuracy and 0.93 F1-score in SV images, decreasing to 0.63 accuracy and 0.66 F1-score in MP images. Overall, this study demonstrates that combining detection and segmentation approaches provides complementary information for urban vegetation monitoring, supporting decision-making related to invasive species management and sustainable urban landscape planning.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEspécies de árvores invasoras
dc.subjectDetecção de espécies de árvores urbanas
dc.subjectSegmentação semântica
dc.subjectYOLOv8
dc.subjectDetection Transformer
dc.subjectSegment Anything Model
dc.subjectPlanejamento urbano
dc.subjectAprendizado profundo
dc.subjectImagem aérea
dc.subjectInvasive tree species
dc.subjectUrban tree species detection
dc.subjectSemantic segmentation
dc.titleIdentification of Leucaena leucocephala in urban landscapes using Street-level images and deep learning.
dc.typeArtigo de periódico
dc.subject.nalthesaurusUrban planning
riaa.ainfo.id1186025
riaa.ainfo.lastupdate2026-04-02
dc.identifier.doihttps://doi.org/10.3390/urbansci10040192
dc.contributor.institutionDANIELLE ELIS GARCIA FURUYA, UNIVERSIDADE DO OESTE PAULISTA; GLEISON MARRAFON, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; EDUARDO LOPES DE LEMOS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MICHELLE TAIS GARCIA FURUYA, UNIVERSIDADE DO OESTE PAULISTA; ROBSON DIEGO SILVA GONÇALVES, UNIVERSIDADE DO OESTE PAULISTA; WESLEY NUNES GONÇALVES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; VERALDO LIESENBERG, UNIVERSIDADE DO ESTADO DE SANTA CATARINA; LUCAS PRADO OSCO, UNIVERSIDADE DO OESTE PAULISTA; ANA PAULA MARQUES RAMOS, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO".
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