@Article{FerreiraQVMSPCCGSSAFNCZZKSCF:2020:ReMePr,
               author = "Ferreira, Karine Reis and Queiroz, Gilberto Ribeiro and Vinhas, 
                         Lubia and Marujo, Rennan de Freitas Bezerra and Sim{\~o}es, Rolf 
                         Ezequiel de Oliveira and Picoli, Michelle Cristina Ara{\'u}jo and 
                         Camara, Gilberto and Cartaxo, Ricardo and Gomes, Vitor Conrado 
                         Faria and Santos, Lorena Alves dos and Sanchez Ipia, Alber 
                         Hamersson and Arcanjo, Jeferson de Souza and Fronza, Jos{\'e} 
                         Guilherme and Noronha, Carlos Alberto and Costa, Raphael Willian 
                         da and Zaglia, Matheus Cavassan and Zioti, Fabiana and 
                         K{\"o}rting, Thales Sehn and Soares, Anderson Reis and Chaves, 
                         Michel Eust{\'a}quio Dantas and Fonseca, Leila Maria Garcia",
          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)} 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)} 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)} 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)} 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 = "Earth Observation Data Cubes for Brazil: Requirements, Methodology 
                         and Products",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
                pages = "e4033",
             keywords = "analysis-ready data, data cubes, image time series analysis, 
                         machine learning, land use and cover mapping.",
             abstract = "Recently, remote sensing image time series analysis has being 
                         widely used to investigate the dynamics of environments over time. 
                         Many studies have combined image time series analysis with machine 
                         learning methods to improve land use and cover change mapping. In 
                         order to support image time series analysis, analysis-ready data 
                         (ARD) image collections have been modeled and organized as 
                         multidimensional data cubes. Data cubes can be defined as sets of 
                         time series associated with spatially aligned pixels. Based on 
                         lessons learned in the research project e-Sensing, related to 
                         national demands for land use and cover monitoring and related to 
                         state-of-the-art studies on relevant topics, we define the 
                         requirements to build Earth observation data cubes for Brazil. 
                         This paper presents the methodology to generate ARD and 
                         multidimensional data cubes from remote sensing images for Brazil. 
                         We describe the computational infrastructure that we are 
                         developing in the Brazil Data Cube project, composed of software 
                         applications and Web services to create, integrate, discover, 
                         access, and process the data sets. We also present how we are 
                         producing land use and cover maps from data cubes using image time 
                         series analysis and machine learning techniques.",
                  doi = "10.3390/rs12244033",
                  url = "http://dx.doi.org/10.3390/rs12244033",
                 issn = "2072-4292",
             language = "en",
           targetfile = "ferreira_earth.pdf",
        urlaccessdate = "28 abr. 2024"
}