@Article{ArcanjoLuzFazeRamo:2016:MeEvVo,
               author = "Arcanjo, Jeferson de Souza and Luz, Eduardo F. P. and Fazenda, 
                         {\'A}lvaro L. and Ramos, Fernando Manuel",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National 
                         Center for Monitoring and Early Warning of Natural Disasters 
                         (Cemaden)} and {Universidade Federal de S{\~a}o Paulo (UNIFESP)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Methods for evaluating volunteers’ contributions in a 
                         deforestation detection citizen science project",
              journal = "Future Generation Computer Systems",
                 year = "2016",
               volume = "56",
                pages = "550--557",
                month = "Mar.",
             keywords = "Citizen science, Data analysis and validation, Forest 
                         monitoring.",
             abstract = "Today, due to the availability of free remote sensing data, 
                         efficient algorithms for image classification and increased 
                         connectivity and computing power, together with international 
                         policy initiatives, such as the United Nations Programme on 
                         Reducing Emissions from Deforestation and Forest Degradation 
                         (UN-REDD), more and more countries are investing in their own 
                         national forest monitoring schemes. However, tropical forests 
                         remain under threat worldwide. Recently, a citizen science project 
                         that enables citizens around the globe to be involved in forest 
                         monitoring tasks has been proposed, called ForestWatchers 
                         (www.forestwatchers.net). Its main goal is to allow volunteers 
                         (many of them with no scientific training) around the globe, with 
                         their own smartphones, tablets and notebooks, review satellite 
                         images of forested regions and confirm whether automatic 
                         assignments of forested and deforested regions are correct. 
                         Inspected images are then sent to a central database where the 
                         results are integrated to generate up-to-date deforestation maps. 
                         This approach offers a low-cost way to both strengthen the 
                         scientific infrastructure and engage members of the public in 
                         science. Here, we describe the methods developed within the scope 
                         of the ForestWatchers project to assess the volunteers 
                         performance. These tools have been evaluated with data of two of 
                         the projects preliminary tasks. The first, called BestTile, asks 
                         volunteers to select which of several images of the same area has 
                         the least cloud cover, while in the second, called Deforestation, 
                         volunteers draw polygons on satellite images delimiting areas they 
                         believe have been deforested. The results from more than 500 
                         volunteers show that using simple statistical tests, it is 
                         possible to achieve a triple goal: to increase the overall 
                         efficiency of the data collecting tasks by reducing the required 
                         number of volunteers per task, to identify malicious behavior and 
                         outliers, and to motivate volunteers to continue their 
                         contributions.",
                  doi = "10.1016/j.future.2015.07.005",
                  url = "http://dx.doi.org/10.1016/j.future.2015.07.005",
                 issn = "0167-739X",
             language = "en",
           targetfile = "arcanjo_methods.pdf",
        urlaccessdate = "27 abr. 2024"
}