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Integration statistical systems for land cover mapping in Southern Brazil
Artur Ricardo Bizon
Luciana P. de Araújo Kohler
Adilson Luiz Nicoletti
Fernanda Dal Bosco
Murilo Schramm da Silva
Thales Bohn Pessatti
Amanda Hughes
Fiona McNeill
Christopher W. Zobel
The remote sensing is a way to optimize the process of land cover classification allowing that this process will be by high definition images of satellite. For the research it was used the Google Earth Engine with JavaScript programming language to classify the images, identifying the areas with forest or reforest. It was identified that classifiers Random Forest and Logistic Regression have a high performance in classify the images. From them it was developed functions to process automatically of new images with purpose of classify them in relation to land cover.
urn:ISBN:2411-3433
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Integration%20statistical%20systems%20for%20land%20cover%20mapping%20in%20Southern%20Brazil&stitle=Iscram%202020&issn=978-1-949373-27-47&isbn=2411-3433&date=2020&spage=498&epage=505&aulast=Artur%20Ricardo%20Bizon&au=Luciana%20P.%20de%20Ara%FAjo%20Kohler&au=Adilson%20Luiz%20Nicoletti&au=Fernanda%20Dal%20Bosco&au=Murilo%20Schramm%20da%20Silva&au=Thales%20Bohn%20Pessatti&pub=Virginia%20Tech&place=Blacksburg%2C%20VA%20%28USA%29&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=2248
citekey:ArturRicardoBizon_etal2020
citation:Artur Ricardo Bizon, Luciana P. de Araújo Kohler, Adilson Luiz Nicoletti, Fernanda Dal Bosco, Murilo Schramm da Silva, & Thales Bohn Pessatti. (2020). Integration statistical systems for land cover mapping in Southern Brazil. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 498-505). Blacksburg, VA (USA): Virginia Tech.
2020
ConferencePaper
text
Random Forest, Logistic Regression, Classifier, Google Earth Engine, Remote Sensing.
file:http://idl.iscram.org/files/arturricardobizon/2020/2248_ArturRicardoBizon_etal2020.pdf
Virginia Tech
English
978-1-949373-27-47
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
2020
498
505
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