| 1. Identity statement | |
| Reference Type | Conference Paper (Conference Proceedings) |
| Site | plutao.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP3W/3MTMTHU |
| Repository | sid.inpe.br/plutao/2016/12.05.18.46.20 |
| Last Update | 2016:12.09.15.05.02 (UTC) lattes |
| Metadata Repository | sid.inpe.br/plutao/2016/12.05.18.46.21 |
| Metadata Last Update | 2018:06.21.04.25.16 (UTC) administrator |
| DOI | 10.13140/RG.2.2.26048.12805 |
| Label | lattes: 2916855460918534 4 KortingNamiFonsFelg:2016:HOEFOB |
| Citation Key | KörtingNamiFonsFelg:2016:HoEfOb |
| Title | How to effectively obtain metadata from remote sensing big data?  |
| Format | DVD |
| Year | 2016 |
| Access Date | 2024, Apr. 24 |
| Secondary Type | PRE CI |
| Number of Files | 1 |
| Size | 500 KiB |
| 2. Context | |
| Author | 1 Körting, Thales Sehn 2 Namikawa, Laércio Massaru 3 Fonseca, Leila Maria Garcia 4 Felgueiras, Carlos Alberto |
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHL5 3 8JMKD3MGP5W/3C9JHLD 4 8JMKD3MGP5W/3C9JGQD |
| Group | 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 |
| Affiliation | 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) |
| Author e-Mail Address | 1 thales.korting@inpe.br 2 laercio.namikawa@inpe.br 3 leila.fonseca@inpe.br 4 carlos.felgueiras@inpe.br |
| Conference Name | GEOBIA 2016 Solutions and Synergies |
| Conference Location | Enschede, The Nederlands |
| Date | 14-16 set. |
| Book Title | Proceedings |
| Tertiary Type | Paper |
| History (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. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Version Type | publisher |
| Keywords | Big data Remote Sensing Metadata Image Processing Water indices Pattern recognition |
| 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 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). |
| Area | SRE |
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > How to effectively... |
| Arrangement 2 | urlib.net > CGOBT > How to effectively... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| data URL | http://urlib.net/ibi/8JMKD3MGP3W/3MTMTHU |
| zipped data URL | http://urlib.net/zip/8JMKD3MGP3W/3MTMTHU |
| Language | en |
| Target File | korting_how.pdf |
| Reader Group | administrator lattes |
| Visibility | shown |
| Read Permission | allow from all |
| Update Permission | not transferred |
| 5. Allied materials | |
| Mirror Repository | urlib.net/www/2011/03.29.20.55 |
| Next Higher Units | 8JMKD3MGPCW/3EQCCU5 8JMKD3MGPCW/3EU2H28 |
| Citing Item List | sid.inpe.br/bibdigital/2013/10.01.23.43 1 sid.inpe.br/mtc-m21/2012/07.13.14.43.05 1 |
| URL (untrusted data) | https://www.conftool.net/geobia2016/index.php?page=browseSessions&abstracts=show&form_session=16&presentations=show |
| Host Collection | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notes | |
| Notes | 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).. |
| Empty Fields | 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. Description control | |
| e-Mail (login) | simone |
| update | |