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@InProceedings{AssisFerVinNovZip:2018:DiCrGe,
               author = "Assis, Luiz Fernando Ferreira Gomes de and Ferreira, Karine Reis 
                         and Vinhas, L{\'u}bia and Novack, Tessio and Zipf, Alexander",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Heidelberg University} and 
                         {Heidelberg University}",
                title = "A discussion of crowdsourced geographic information initiatives 
                         and big Earth observation data architectures for land-use and 
                         land-cover monitoring",
                 year = "2018",
         organization = "VGI-ALIVE: Analysis Integration, Vision, Engagement",
             keywords = "Citizen Science, Land-Use/Land-Cover, Remote Sensing, Big Earth 
                         Observation Data.",
             abstract = "The effective monitoring of land-use and land-cover changes 
                         (LULCC) is a basic requirement for understanding 
                         socio-enviromental processes of local to global scales. Remote 
                         sensing data and methods have long been established as the most 
                         effective approach for monitoring LULCC. The potential for further 
                         increasing the effectiveness of this approach is proportional to 
                         the astonishingly large amount of satellite imagery provided, many 
                         times free-of-cost, by space agencies worldwide. However, 
                         scientists still lack of ways of organizing, structuring and 
                         analyzing this colossal amount of remote sensing data in a way 
                         that leverages administrative and scientific LULCC monitoring. 
                         Hence, an efficient image data storage, query and processing 
                         architecture that manages different satellite specifications and 
                         climatic conditions is required for generating and sharing updated 
                         and area-extensive LULCC information. Furthermore, because 
                         reliable LULCC monitoring with remote sensing data requires 
                         extensive training and validation analysis performed by humans, 
                         the potential of big Earth Observation (EO) data for LULCC 
                         monitoring is still limited by the amount and time- availability 
                         of the analysts involved in the project. In this paper, we discuss 
                         the potential of Citizen Science for improving the feasibility and 
                         effectiveness of LULCC monitoring supported by big EO data 
                         architectures. We put forward general ideas on how to promote and 
                         stimulate an active involvement of citizens in EO data analytics 
                         for LULCC monitoring. For that, we briefly present and critically 
                         evaluate how existing approaches that allow citizens to contribute 
                         with up-to-date and detailed LULCC information mitigate the issue 
                         of exhaustive sampling required in LULCC monitoring with automatic 
                         remote sensing image classification.",
  conference-location = "Lund, Sweden",
      conference-year = "12 june",
             language = "en",
                  ibi = "8JMKD3MGP3W34R/3RCCJB5",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3RCCJB5",
           targetfile = "assis_discussion.pdf",
        urlaccessdate = "01 dez. 2020"
}


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