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@InProceedings{BittencourtMoreSantSant:2019:EvClMo,
               author = "Bittencourt, Olga Oliveira and Morelli, Fabiano and Santos 
                         J{\'u}nior, C{\'{\i}}cero Alves dos and Santos, Rafael Duarte 
                         Coelho dos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Evaluating classification models in a burning areas' detection 
                         approach",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Misra, Sanjay and Gervasi, Osvaldo and Murgante, Beniamino and 
                         Stankova, Elena and Korkhov, Vladimir and Torre, Carmelo and 
                         Rocha, Ana Maria A. C. and Taniar, David and Apduhan, Bernady O. 
                         and Tarantino, Eufemia",
         organization = "International Conference on Computational Science and its 
                         Applications",
            publisher = "Springer",
             keywords = "Burned areas, Classification models, Remote sensing data.",
             abstract = "We present a study to improve automation and accuracy on a Woody 
                         Savannah burned areas classification process through the use of 
                         Machine Learning (ML) classification models. The reference method 
                         for this is to extract polygons from images through segmentation 
                         and identify changes in polygons extracted from images taken from 
                         the same area but in different times through manual labeling. 
                         However, not all differences correspond to burned areas: there are 
                         also deforestation, change in crops, and clouds. Our objective is 
                         to identify the changed areas caused by fire. We propose an 
                         approach that employs polygons attributes for classification and 
                         evaluation in order to identify changes caused by fire. This paper 
                         presents the more relevant classifier models to the problem, 
                         highlighting Random Forest and an Ensemble model, that achieved 
                         better results. The developed approach is validated over a study 
                         area in the Brazilian Woody Savannah against reference data 
                         derived from classifications manually done by experts. The results 
                         indicate enhancement of the methods used so far, and will 
                         eventually be applied to more data from different areas and 
                         biomes.",
  conference-location = "Saint Petersburg, Russia",
      conference-year = "01-04 July",
                  doi = "10.1007/978-3-030-24305-0_43",
                  url = "http://dx.doi.org/10.1007/978-3-030-24305-0_43",
                 isbn = "978-303024304-3",
                 issn = "03029743",
             language = "en",
        urlaccessdate = "29 nov. 2020"
}


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