%0 Conference Proceedings
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@nexthigherunit 8JMKD3MGPCW/3EU29DP
%@nexthigherunit 8JMKD3MGPCW/3F2PHGS
%2 sid.inpe.br/mtc-m21c/2020/10.26.17.22.02
%4 sid.inpe.br/mtc-m21c/2020/10.26.17.22
%3 amita_total.pdf
%8 01-04 July
%@issn 03029743
%A Muralikrishna, Amita,
%A Vieira, Luis Eduardo Antunes,
%A Santos, Rafael Duarte Coelho dos,
%A Almeida, Adriano Pereira,
%B International Conference on Computational Science and Its Applications,20
%@secondarytype PRE CI
%C Cagliari, Italy
%D 2020
%E Gervasi, O.,
%E Murgante, B.,
%E Misra, S.,
%E Garau, C.,
%E Blecic, I.,
%E Taniar, D.,
%E Apduhan, B. O.,
%E Rocha, A. M. A. C.,
%E Tarantino, E.,
%E Torre, C. M.,
%E Karaca, Y.,
%@secondarykey INPE--PRE/
%I Springer
%K Remote sensing · Burned forest classification keyword · Forest fire survey and monitoring.
%O Lecture Notes in Computer Science, v.12253
%P 255-269
%S Proceedings
%T Total solar irradiance forecasting with keras recurrent neural networks
%X Monitoring the large number of active fires and their consequences in such an extensive area such as the Brazilian territory is an important task. Machine Learning techniques are a promising approach to contribute to this area, but the challenge is the building of rich example datasets, whose previous examples are unavailable in many areas. Our aim in this article is to move towards the development of an approach to detect burned areas in regions for which there is no previously validated samples. We deal with that by presenting some experiments to classify burned areas through Machine Learning techniques that combine remote sensing data from nearby areas and it can distinguish between burned and non burned polygons with good results.
%@area COMP
%@electronicmailaddress amita@ifsp.edu.br
%@electronicmailaddress luis.vieira@inpe.br
%@electronicmailaddress cicero.junior@inpe.br
%@electronicmailaddress adriano.almeida@inpe.br
%@documentstage not transferred
%@group LABAC-COCTE-INPE-MCTIC-GOV-BR
%@group DIDGE-CGCEA-INPE-MCTIC-GOV-BR
%@group LABAC-COCTE-INPE-MCTIC-GOV-BR
%@group CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
%@orcid 0000-0001-9669-0576
%@orcid 0000-0002-9376-475X
%@orcid 0000-0002-8313-6688
%@orcid 0000-0001-9605-9805
%@usergroup simone
%@isbn 978-303058813-7
%@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)
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JJ4N
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1007/978-3-030-58814-4_18