author = "Cunha, John Elton de Brito Leite and Rufino, Iana Alexandra Alves 
                         and Galv{\~a}o, Carlos de Oliveira and Perreira, Thiago Emmanuel 
                         and Brasileiro, Francisco Vilar and Perreira, Esdras Vidal",
                title = "An{\'a}lise e Processamento Autom{\'a}tico de Grandes Volumes de 
                         Dados Ambientais (Big Earth Observation Data Sets)",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7459--7466",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Hydrology and water resources demand monitoring land use and 
                         cover, related to the impacts of climate and human action. 
                         However, very often data for such monitoring and sequent analysis 
                         are from spatial scales that cannot be fully collected by field 
                         survey. Remote sensing techniques and data are suitable to those 
                         needs, since include land use/land cover changes detection in 
                         different scales (from local to continental landscapes). This 
                         paper presents an intercontinental initiative: the EUBrazil Cloud 
                         Connect project, developed by European and Brazilian partners. The 
                         main goal is to provide a cloud computing infrastructure to use 
                         tools for multi-temporal analysis and trend analysis of huge 
                         remote sensing databases to understand the main current drivers of 
                         land use changes. SEBAL (Surface Energy Balance Algorithm for 
                         Land) algorithm has been processed for a long time series (more 
                         than 30 years of satellite images) covering the whole Brazilian 
                         semi-arid area. Web services for visualization, analysis and 
                         deployment for decision makers and researchers are used.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59372",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMFPP",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3PSMFPP",
           targetfile = "59372.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
        urlaccessdate = "22 jan. 2021"