| 1. Identificação | |
| Tipo de Referência | Artigo em Evento (Conference Proceedings) |
| Site | plutao.sid.inpe.br |
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identificador | 8JMKD3MGP3W/3MTMTHU |
| Repositório | sid.inpe.br/plutao/2016/12.05.18.46.20 |
| Última Atualização | 2016:12.09.15.05.02 (UTC) lattes |
| Repositório de Metadados | sid.inpe.br/plutao/2016/12.05.18.46.21 |
| Última Atualização dos Metadados | 2018:06.21.04.25.16 (UTC) administrator |
| DOI | 10.13140/RG.2.2.26048.12805 |
| Rótulo | lattes: 2916855460918534 4 KortingNamiFonsFelg:2016:HOEFOB |
| Chave de Citação | KörtingNamiFonsFelg:2016:HoEfOb |
| Título | How to effectively obtain metadata from remote sensing big data?  |
| Formato | DVD |
| Ano | 2016 |
| Data de Acesso | 16 maio 2024 |
| Tipo Secundário | PRE CI |
| Número de Arquivos | 1 |
| Tamanho | 500 KiB |
| 2. Contextualização | |
| Autor | 1 Körting, Thales Sehn 2 Namikawa, Laércio Massaru 3 Fonseca, Leila Maria Garcia 4 Felgueiras, Carlos Alberto |
| Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JHL5 3 8JMKD3MGP5W/3C9JHLD 4 8JMKD3MGP5W/3C9JGQD |
| Grupo | 1 DPI-OBT-INPE-MCTI-GOV-BR 2 DPI-OBT-INPE-MCTI-GOV-BR 3 OBT-OBT-INPE-MCTI-GOV-BR 4 DPI-OBT-INPE-MCTI-GOV-BR |
| Afiliação | 1 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) |
| Endereço de e-Mail do Autor | 1 thales.korting@inpe.br 2 laercio.namikawa@inpe.br 3 leila.fonseca@inpe.br 4 carlos.felgueiras@inpe.br |
| Nome do Evento | GEOBIA 2016 Solutions and Synergies |
| Localização do Evento | Enschede, The Nederlands |
| Data | 14-16 set. |
| Título do Livro | Proceedings |
| Tipo Terciário | Paper |
| Histórico (UTC) | 2016-12-05 19:23:58 :: lattes -> administrator :: 2016 2016-12-09 07:36:09 :: administrator -> lattes :: 2016 2016-12-22 16:51:11 :: lattes -> administrator :: 2016 2018-06-21 04:25:16 :: administrator -> simone :: 2016 |
| 3. Conteúdo e estrutura | |
| É a matriz ou uma cópia? | é a matriz |
| Estágio do Conteúdo | concluido |
| Transferível | 1 |
| Tipo do Conteúdo | External Contribution |
| Tipo de Versão | publisher |
| Palavras-Chave | Big data Remote Sensing Metadata Image Processing Water indices Pattern recognition |
| Resumo | What can be considered big data when dealing with remote sensing imagery? In general terms, big data is defined as data requiring high management capabilities characterized by 3 Vs: Volume, Velocity and Variety. In the past, (e.g. 1975), considering the computational and databases resources available, a series of Landsat-1 imagery from the same region could be considered big data. Nowadays, several satellites are available, and they produce massive amounts of data. Certainly, an image data set obtained by a single satellite, for a specific region and along time, fills the 3 Vs requirements to be considered big data as well. In order to deal with remote sensing big data, we propose to explore the generation of metadata based on the detection of simple features. Besides the intrinsic geographic information on every remote sensing scene, no additional metadata is usually considered. We propose basic image processing algorithms to detect basic well-known patterns, and include them as tags, such as cloud, shadow, stadium, vegetation, and water, according to what is detectable at each spatial resolution. In this work we show preliminary results using imagery from RapidEye sensor, with 5 meter spatial resolution, composed by two full coverages of Brazil with RapidEye multispectral imagery (around 40k scenes). |
| Área | SRE |
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > How to effectively... |
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > CGOBT > How to effectively... |
| Conteúdo da Pasta doc | acessar |
| Conteúdo da Pasta source | não têm arquivos |
| Conteúdo da Pasta agreement | não têm arquivos |
| 4. Condições de acesso e uso | |
| URL dos dados | http://urlib.net/ibi/8JMKD3MGP3W/3MTMTHU |
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP3W/3MTMTHU |
| Idioma | en |
| Arquivo Alvo | korting_how.pdf |
| Grupo de Leitores | administrator lattes |
| Visibilidade | shown |
| Permissão de Leitura | allow from all |
| Permissão de Atualização | não transferida |
| 5. Fontes relacionadas | |
| Repositório Espelho | urlib.net/www/2011/03.29.20.55 |
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3EQCCU5 8JMKD3MGPCW/3EU2H28 |
| Lista de Itens Citando | sid.inpe.br/mtc-m21/2012/07.13.14.53.18 3 sid.inpe.br/bibdigital/2013/10.01.23.43 2 sid.inpe.br/mtc-m21/2012/07.13.14.43.05 2 |
| URL (dados não confiáveis) | https://www.conftool.net/geobia2016/index.php?page=browseSessions&abstracts=show&form_session=16&presentations=show |
| Acervo Hospedeiro | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notas | |
| Notas | Setores de Atividade: Atividades dos serviços de tecnologia da informação. Informações Adicionais: ABSTRACT: What can be considered big data when dealing with remote sensing imagery? In general terms, big data is defined as data requiring high management capabilities characterized by 3 V?s: Volume, Velocity and Variety. In the past, (e.g. 1975), considering the computational and databases resources available, a series of Landsat-1 imagery from the same region could be considered big data. Nowadays, several satellites are available, and they produce massive amounts of data. Certainly, an image data set obtained by a single satellite, for a specific region and along time, fills the 3 V?s requirements to be considered big data as well. In order to deal with remote sensing big data, we propose to explore the generation of metadata based on the detection of simple features. Besides the intrinsic geographic information on every remote sensing scene, no additional metadata is usually considered. We propose basic image processing algorithms to detect basic well-known patterns, and include them as tags, such as cloud, shadow, stadium, vegetation, and water, according to what is detectable at each spatial resolution. In this work we show preliminary results using imagery from RapidEye sensor, with 5 meter spatial resolution, composed by two full coverages of Brazil with RapidEye multispectral imagery (around 40k scenes).. |
| Campos Vazios | archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress edition editor isbn issn lineage mark nextedition numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark type usergroup volume |
| 7. Controle da descrição | |
| e-Mail (login) | simone |
| atualizar | |
