@Article{BragaPDFTACSW:2020:TrCrDe,
               author = "Braga, Jos{\'e} Renato Garcia and Peripato, Vin{\'{\i}}cius 
                         Borges Pereira and Dalagnol da Silva, Ricardo and Ferreira, 
                         Matheus P. and Tarabalka, Yuliya and Arag{\~a}o, Luiz Eduardo 
                         Oliveira e Cruz de and Campos Velho, Haroldo Fraga de and 
                         Shiguemori, Elcio H. and Wagner, Fabien Hubert",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Militar de Engenharia 
                         (IME)} and {Inria Sophia Antipolis} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto de Estudos Avan{\c{c}}ado 
                         (IEAv)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Tree crown delineation algorithm based on a convolutional neural 
                         network",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "8",
                pages = "e1288",
                month = "Apr.",
             keywords = "tree crown delineation, tropical forests, optical satellite 
                         images, deep learning.",
             abstract = "Tropical forests concentrate the largest diversity of species on 
                         the planet and play a key role in maintaining environmental 
                         processes. Due to the importance of those forests, there is 
                         growing interest in mapping their components and getting 
                         information at an individual tree level to conduct reliable 
                         satellite-based forest inventory for biomass and species 
                         distribution qualification. Individual tree crown information 
                         could be manually gathered from high resolution satellite images; 
                         however, to achieve this task at large-scale, an algorithm to 
                         identify and delineate each tree crown individually, with high 
                         accuracy, is a prerequisite. In this study, we propose the 
                         application of a convolutional neural networkMask R-CNN 
                         algorithmto perform the tree crown detection and delineation. The 
                         algorithm uses very high-resolution satellite images from tropical 
                         forests. The results obtained are promisingthe Recall, Precision, 
                         and F1 score values obtained were were 0.81, 0.91, and 0.86, 
                         respectively. In the study site, the total of tree crowns 
                         delineated was 59, 062. These results suggest that this algorithm 
                         can be used to assist the planning and conduction of forest 
                         inventories. As the algorithm is based on a Deep Learning 
                         approach, it can be systematically trained and used for other 
                         regions.",
                  doi = "10.3390/RS12081288",
                  url = "http://dx.doi.org/10.3390/RS12081288",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-12-01288-v3.pdf",
        urlaccessdate = "2024, Apr. 18"
}