@InProceedings{SantosOliKörAdaSan:2023:SeMuDa,
               author = "Santos, Priscilla Azevedo dos and Oliveira, Maria Ant{\^o}nia 
                         Falc{\~a}o de and K{\"o}rting, Thales Sehn and Adami, Marcos and 
                         Sanches, Ieda Del'Arco",
          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)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Sentinel-2 multidimensional data cubes for crop monitoring time 
                         series classification",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155710",
         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 = "Satellite time series, MSI, crop maps, land use, monitoring.",
             abstract = "Geoprocessing and remote sensing play an important role when it 
                         comes to monitoring land use and land cover using large volumes of 
                         data (Big data). In this context, Satellite Time Series Image 
                         (Data Cubes) emerge as an alternative to manage Big data mining 
                         and classification. Combining information and describing data 
                         using time series analysis methods, like Time-Weighted Dynamic 
                         Time Warping (TWDTW), for pattern recognition and classification 
                         in diverse areas, becomes possible to observe and understand land 
                         use and land cover changes as agricultural expansion and crop 
                         monitoring. Thus, this work aims to classify crops dynamics in the 
                         western portion of Bahia - Brazil, using machine learning and data 
                         cubes. Our results showed consistency and feasibility in mapping 
                         agricultural targets on a monthly base, with a reasonable 
                         classification accuracy over 70% for the produced maps.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/495D2D2",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/495D2D2",
           targetfile = "155710_compressed.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "17 jun. 2024"
}