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@Article{SantosPiOlKöEsAm:2022:IdPrSe,
                 year = "2022",
               volume = "14",
                 issn = "2072-4292",
               author = "Santos, Bruno Dias dos and Pinho, Carolina Moutinho Duque de and 
                         Oliveira, Gilberto Eidi Teramoto and K{\"o}rting, Thales Sehn and 
                         Escada, Maria Isabel Sobral and Amaral, Silvana",
                title = "Identifying precarious settlements and urban fabric typologies 
                         based on GEOBIA and data mining in brazilian Amazon cities",
                  doi = "10.3390/rs14030704",
                  url = "http://dx.doi.org/10.3390/rs14030704",
             keywords = "Amazonian precarious settlements, Amazonian urbanization, GEOBIA, 
                         data mining, urban fabric typology.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do ABC (UFABC)} 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)}",
             abstract = "Although 70% of the Amazon population lives in urban areas, 
                         studies on the urban Amazon are scarce. Much of the urban Amazon 
                         population lives in precarious settlements. The distinctiveness 
                         and diversity of Amazonian precarious settlements are vast and 
                         must be identified to be considered in the development of 
                         appropriate public policies. Aiming at investigating precarious 
                         settlements in Amazon, this study is guided by the following 
                         questions: For the Brazilian Amazon region, is it possible to 
                         identify areas of precarious settlements by combining 
                         geoprocessing and remote sensing techniques? Are there different 
                         typologies of precarious settlements distinguishable by their 
                         spatial arrangements? Thus, we developed a methodology for 
                         identifying precarious settlements and subsequently classifying 
                         them into urban fabric typologies (UFT), choosing the cities of 
                         Altamira, Camet{\'a}, and Marab{\'a} as study sites. Our 
                         classification model utilized geographic objects-based image 
                         analysis (GEOBIA) and data mining of spectral data from WPM sensor 
                         images from the CBERS-4A satellite, jointly with texture metrics, 
                         context metrics, biophysical index, voluntary geographical 
                         information, and neighborhood relationships. With the C5.0 
                         decision tree algorithm we carried out variable selection and 
                         classification of these geographic objects. Our estimated models 
                         show accuracy above 90% when applied to the study sites. 
                         Additionally, we described Amazonian UFT in six types to be 
                         identified. We concluded that Amazonian precarious settlements are 
                         morphologically diverse, with an urban fabric different from those 
                         commonly found in Brazilian metropolitan areas. Identifying and 
                         characterizing distinct precarious areas is vital for the planning 
                         and development of sustainable and effective public policies for 
                         the urban Amazon.",
              journal = "Remote Sensing",
               number = "3",
                pages = "e704",
           targetfile = "remotesensing-14-00704.pdf",
             language = "en",
        urlaccessdate = "30 jun. 2022"
}


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