%0 Conference Proceedings
%4 sid.inpe.br/mtc-m16c/2022/12.16.14.37
%2 sid.inpe.br/mtc-m16c/2022/12.16.14.37.32
%@issn 2179-4847
%T Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
%D 2022
%A Gino, Vinícius L. S.,
%A Negri, Rogério,
%A Souza, Felipe N.,
%@affiliation Universidade Estadual Paulista (UNESP)
%@affiliation Universidade Estadual Paulista (UNESP)
%@affiliation Universidade Estadual Paulista (UNESP)
%@electronicmailaddress vinicius.gino@unesp.br
%@electronicmailaddress rogerio.negri@unesp.br
%@electronicmailaddress fn.souza@unesp.br
%E Rosim, Sergio (INPE),
%E Santos, Leonardo Bacelar Lima (CEMADEN),
%E Pereira, Marconi de Arruda (UFSJ),
%B Simpósio Brasileiro de Geoinformática, 23 (GEOINFO)
%C On-line
%8 28 a 30 nov. 2022
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%S Anais
%X In climate changes context Remote Sensing tools are widely used and widespread in research. In this sense, Artificial Intelligence rises offering possible improves for environmental monitoring applications using techniques such as Machine Learning for Anomaly Detection applied to Remote Sensing imagery to identify the spatio-temporal changes over the Earths surface. This approach is explored in three high dynamic regions in Brazil assessing deforestation, fires and technological disaster areas using One-Class SVM and Isolation Forest methods over MODIS, Landsat and Sentinel images.
%@language en
%3 99-110_Gino_remote.pdf