@InProceedings{Simões:2019:LaUsLa,
               author = "Sim{\~o}es, Rolf Ezequiel de Oliveira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Land use and land cover classification of satellite image time 
                         series using machine learning",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Santos, R. D. C. and Queiroz, G. R.",
         organization = "Workshop de Computa{\c{c}}{\~a}o Aplicada, 19. (WORCAP)",
             abstract = "The human activities are impacting the global environment and the 
                         Earth surface. Research on new technologies to assess and monitor 
                         this impact is a necessary task to improve our knowledge on Earth 
                         system dynamics. One way to understand the environmental changes 
                         is to look to land cover and land use changes. In the past few 
                         decades, the Earth surface imaging done by orbital sensors is the 
                         most consistent way to do this task globally and periodically. 
                         Nowadays, the planet is continuously monitored and a several 
                         imagery databases are open to the public community. This massive 
                         volume of spatio-temporal suggested the concept of big Earth 
                         Observation data that, associated with the recent innovations on 
                         information technologies and connectivity, increased the attention 
                         to the Earth Observation data cubes (EODC) (Strobl, 2017; Giuliani 
                         et al., 2019). More than a way to organize multidimensional data, 
                         an EODC can be viewed as a package of solutions intended to 
                         facilitate its consumption by researchers. Recently, some private 
                         and governmental initiatives by research groups and institutions 
                         worldwide are producing and delivering EODC. INPE, the National 
                         Institute for Space Research in Brazil, is working on a 
                         challenging project, organize and deliver the Brazilian Data Cube 
                         (BDC) (INPE, 2019). Here, we present a work in progress model of 
                         the BDC catalog database using the Unified Modeling Language (UML) 
                         to clarifies some concepts and propose its Entity-Relationship 
                         model. The BDC catalog database is a central component of the data 
                         cube technology. It enables the search and retrieval of its data 
                         elements and facilitates the interoperability between services and 
                         client softwares.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
      conference-year = "17-19 set. 2019",
             language = "en",
           targetfile = "Simoes_land.pdf",
        urlaccessdate = "03 maio 2024"
}