@InProceedings{SilvaFonsMell:2015:ReBaMa,
               author = "Silva, Alexsandro C{\^a}ndido de Oliveira and Fonseca, Leila 
                         Maria Garcia and Mello, Marcio Pupin",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Redes Bayesianas no mapeamento de culturas de ver{\~a}o no Estado 
                         do Paran{\'a}",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2379--2386",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "In Brazil, the methodologies employed to obtain official 
                         agricultural statistics are subjective, and take a long time to be 
                         realized. Remote sensing technologies, combined with artificial 
                         intelligence, allow quick and accurate outcomes, which may help 
                         these methodologies to be more efficient. This paper aims at 
                         proposing the use of BayNeRD (Bayesian Network for Raster Data) 
                         algorithm to map summer crops areas (soybean and maize) in 
                         Paran{\'a} State Brazil. BayNeRD is a computer-aided Bayesian 
                         Network method that is able to incorporate experts knowledge to 
                         handle with raster data. The main outcome of BayNeRD is a 
                         probability image, wherein each pixel contains the probability of 
                         occurrence of target under study. Based on observations of a 
                         vegetation index, terrain slope, soil aptitude and other 
                         variables, BayNeRD was able to map soybean and maize plantations 
                         in Paran{\'a} State with 82% of sensitivity and 85% of 
                         specificity. Moreover, the probability image showed strong 
                         adherence to the reference data used for accuracy assessment and 
                         to the literature, denoting BayNeRDs potential to be applicable 
                         for agricultural inference through remote sensoring and ancillary 
                         data.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "480",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM49U6",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM49U6",
           targetfile = "p0480.pdf",
                 type = "Geoprocessamento e aplica{\c{c}}{\~o}es",
        urlaccessdate = "27 abr. 2024"
}