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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/43FN8U8
Repositorysid.inpe.br/mtc-m21c/2020/10.26.17.16   (restricted access)
Last Update2020:10.26.17.16.27 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/10.26.17.16.27
Metadata Last Update2022:01.04.01.35.31 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1007/978-3-030-58814-4_17
ISBN978-303058813-7
ISSN03029743
Citation KeyBittencourtMoreSantSant:2020:ApClBu
TitleAn approach to classify burned areas using few previously validated samples
Year2020
Access Date2024, Apr. 18
Secondary TypePRE CI
Number of Files1
Size2816 KiB
2. Context
Author1 Bittencourt, Olga de Oliveira
2 Morelli, Fabiano
3 Santos Júnior, Cícero Alves dos
4 Santos, Rafael Duarte Coelho dos
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JJ4N
ORCID1 0000-0002-1604-3713
2 0000-0003-2110-4477
3 0000-0002-4095-789X
4 0000-0002-8313-6688
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2 DIDSA-CGCPT-INPE-MCTIC-GOV-BR
3 CGCPT-CGCPT-INPE-MCTIC-GOV-BR
4 LABAC-COCTE-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 olga.bittencourt@inpe.br
2 fabiano.morelli@inpe.br
3 cicero.junior@inpe.br
4 rafael.santos@inpe.br
EditorGervasi, O.
Murgante, B.
Misra, S.
Garau, C.
Blecic, I.
Taniar, D.
Apduhan, B. O.
Rocha, A. M. A. C.
Tarantino, E.
Torre, C. M.
Karaca, Y.
Conference NameInternational Conference on Computational Science and Its Applications,20
Conference LocationCagliari, Italy
Date01-04 July
PublisherSpringer
Pages239-254
Book TitleProceedings
History (UTC)2020-10-26 17:16:27 :: simone -> administrator ::
2022-01-04 01:35:31 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsRemote sensing · Burned forest classification keyword · Forest fire survey and monitoring
AbstractMonitoring 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.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > An approach to...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > CGCPT > An approach to...
Arrangement 3urlib.net > DIDSA > An approach to...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target Filebittencourt_approach.pdf
User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3EUPEJL
8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/43SRC6S
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
NotesLecture Notes in Computer Science, v.12253
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress edition format label lineage mark mirrorrepository nextedition numberofvolumes organization parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress readergroup rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume
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