@InProceedings{PrudenteSGOXXAS:2023:LaUsLa,
               author = "Prudente, Victor Hugo Rohden and Silva, Nildson Rodrigues de 
                         Fran{\c{c}}a e and Garcia, Andre Dalla Bernardina and Oldoni, 
                         Lucas Volochen and Xaud, Haron Abrahim Magalh{\~a}es and Xaud, 
                         Maristela Ramalho and Adami, Marcos and Sanches, Ieda Del'Arco",
          affiliation = "{University of Michigan} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Embrapa Roraima} and {Embrapa Roraima} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Land use and land cover classification using a SAR optical cloud 
                         computer approach in southern of Roraima",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156282",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "microwave data, random forest, google engine, amazon region.",
             abstract = "Our goal in this study was to perform a LULC classification for 
                         the southern part of Roraima state. This area has a highly 
                         frequent cloud cover and a lack of LULC information. We used a 
                         SAR-Optical multisensory methodology, with a cloud computing 
                         process, to be able to classify all the areas, with less 
                         computational effort and in less time. Our results show an Overall 
                         Accuracy of 92.61%, with Users' and Producers' Accuracy (UA and 
                         PA), around 90% for all ten classes. Also, this approach 
                         identified important classes for the region, such as perennial 
                         crops and conversion areas.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/494US45",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/494US45",
           targetfile = "156282.pdf",
                 type = "Mudan{\c{c}}a de uso e cobertura da Terra",
        urlaccessdate = "14 maio 2024"
}