@InProceedings{DallaquaFariFaze:2021:CiScMa,
               author = "Dallaqua, Fernanda B. J. R. and Faria, Fabio A. and Fazenda, 
                         {\'A}lvaro L.",
          affiliation = "{Instituto de Ci{\^e}ncia e Tecnologia - Universidade Federal de 
                         S{\~a}o Paulo} and {Instituto de Ci{\^e}ncia e Tecnologia - 
                         Universidade Federal de S{\~a}o Paulo} and {Instituto de 
                         Ci{\^e}ncia e Tecnologia - Universidade Federal de S{\~a}o 
                         Paulo}",
                title = "ForestEyes Project - Citizen Science and Machine Learning to 
                         detect deforested areas in tropical forests",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "citizen science, machine learning, tropical forests, 
                         deforestation, classification.",
             abstract = "The conservation of tropical forests is urgent and necessary due 
                         to the important role they play in the global ecosystem. Several 
                         governmental and private initiatives were created to detect 
                         deforestation in tropical forests through analyses of remote 
                         sensing images, which demands skilled labor and different ways to 
                         deal with a great amount of data. Citizen Science could be used to 
                         mitigate these challenges, as it consists of non-specialized 
                         volunteers collecting, analyzing, and classifying data to solve 
                         technical and scientific problems. In this sense, this work 
                         proposes the ForestEyes Project, which aims to combine citizen 
                         science and machine learning for deforestation detection. The 
                         volunteers classify remote sensing images, and these data are used 
                         as the training set for classification algorithms. The volunteers 
                         classified more than \$5,000\$ tasks from remote sensing images 
                         of the Brazilian Legal Amazon, and the results were compared to a 
                         groundtruth from the Amazon Deforestation Monitoring Project 
                         PRODES. The volunteers achieved good labeling of the remote 
                         sensing data, even for recent deforestation tasks, building 
                         high-confidence labeled collections as they selected the most 
                         relevant samples and discarded noisy segments that might disrupt 
                         machine learning techniques. Finally, the proposed methodology is 
                         promising, and with improvements, it could be able to generate 
                         complementary information to official monitoring programs or even 
                         generate information for areas not yet monitored.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45E5QCE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E5QCE",
           targetfile = "WTD_SIBGRAPI_19.pdf",
        urlaccessdate = "2024, May 06"
}