@Article{WagnerHiry:2019:TrCoYe,
               author = "Wagner, Fabien Hubert and Hirye, Mayumi Cursino de Moura",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Tree cover for the year 2010 of the metropolitan region of 
                         S{\~a}o Paulo, Brazil",
              journal = "Data",
                 year = "2019",
               volume = "4",
               number = "4",
                pages = "e145",
                month = "Dec.",
             keywords = "urban tree cover, urban ecosystem services, image segmentation, 
                         u-net model, deep learning.",
             abstract = "Mapping urban trees with images at a very high spatial resolution 
                         (\≤1 m) is a particularly relevant recent challenge due to 
                         the need to assess the ecosystem services they provide. However, 
                         due to the effort needed to produce these maps from tree censuses 
                         or with remote sensing data, few cities in the world have a 
                         complete tree cover map. Here, we present the tree cover data at 
                         1-m spatial resolution of the Metropolitan Region of S{\~a}o 
                         Paulo, Brazil, the fourth largest urban agglomeration in the 
                         world. This dataset, based on 71 orthorectified RGB aerial 
                         photographs taken in 2010 at 1-m spatial resolution, was produced 
                         using a deep learning method for image segmentation called U-net. 
                         The model was trained with 1286 images of size 64 × 64 pixels at 
                         1-m spatial resolution, containing one or more trees or only 
                         background, and their labelled masks. The validation was based on 
                         322 images of the same size not used in the training and their 
                         labelled masks. The map produced by the U-net algorithm showed an 
                         excellent level of accuracy, with an overall accuracy of 96.4% and 
                         an F1-score of 0.941 (precision = 0.945 and recall = 0.937). This 
                         dataset is a valuable input for the estimation of urban forest 
                         ecosystem services, and more broadly for urban studies or urban 
                         ecological modelling of the S{\~a}o Paulo Metropolitan Region.",
                  doi = "10.3390/data4040145",
                  url = "http://dx.doi.org/10.3390/data4040145",
                 issn = "2306-5729",
             language = "en",
           targetfile = "wagner_tree.pdf",
        urlaccessdate = "20 abr. 2024"
}