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%0 Journal Article
%4 urlib.net/www/2022/02.04.15.51
%2 urlib.net/www/2022/02.04.15.51.53
%@issn 2072-4292
%T Identifying precarious settlements and urban fabric typologies based on GEOBIA and data mining in brazilian Amazon cities
%D 2022
%9 journal article
%A Santos, Bruno Dias dos,
%A Pinho, Carolina Moutinho Duque de,
%A Oliveira, Gilberto Eidi Teramoto,
%A Körting, Thales Sehn,
%A Escada, Maria Isabel Sobral,
%A Amaral, Silvana,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal do ABC (UFABC)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress bruno.santos@inpe.br
%@electronicmailaddress
%@electronicmailaddress gilberto.oliveira@inpe.br
%@electronicmailaddress thales.korting@inpe.br
%@electronicmailaddress Isabel.escada@inpe.br
%@electronicmailaddress silvana.amaral@inpe.br
%B Remote Sensing
%V 14
%N 3
%P e704
%K Amazonian precarious settlements, Amazonian urbanization, GEOBIA, data mining, urban fabric typology.
%X 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, Cametá, and Marabá 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.
%@language en
%3 remotesensing-14-00704.pdf


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