| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/4524AB2 | |||
| Repository | sid.inpe.br/mtc-m21d/2021/07.01.19.39 | |||
| Last Update | 2021:07.01.19.39.49 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2021/07.01.19.39.49 | |||
| Metadata Last Update | 2022:04.03.22.27.25 (UTC) administrator | |||
| DOI | 10.3390/rs13132428 | |||
| ISSN | 2072-4292 | |||
| Citation Key | SimõesCQSASCF:2021:SaImTi | |||
| Title | Satellite Image Time Series Analysis for Big Earth Observation Data  | |||
| Year | 2021 | |||
| Month | June | |||
| Access Date | 2024, May 19 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 5013 KiB | |||
| 2. Context | ||||
| Author | 1 Simões, Rolf Ezequiel de Oliveira 2 Camara, Gilberto 3 Queiroz, Gilberto Ribeiro de 4 Souza, Felipe 5 Andrade, Pedro Ribeiro de 6 Santos, Lorena Alves dos 7 Carvalho, Alexandre 8 Ferreira, Karine Reis | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHB8 3 8JMKD3MGP5W/3C9JHBC 4 5 6 7 8 8JMKD3MGP5W/3C9JHKN | |||
| Group | 1 DIOTG-CGCT-INPE-MCTI-GOV-BR 2 DIOTG-CGCT-INPE-MCTI-GOV-BR 3 DIOTG-CGCT-INPE-MCTI-GOV-BR 4 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 5 DIIAV-CGCT-INPE-MCTI-GOV-BR 6 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 7 8 DIOTG-CGCT-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) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Instituto Nacional de Pesquisas Espaciais (INPE) 7 Instituto de Pesquisas Economicas e Aplicadas (IPEA) 8 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 rolf.simoes@inpe.br 2 gilberto.camara@inpe.br 3 gilberto.queiroz@inpe.br 4 felipe.souza@inpe.br 5 pedro.andrade@inpe.br 6 lorena.santos@inpe.br 7 alexandre.ywata@ipea.gov.br 8 karine.ferreira@inpe.br | |||
| Journal | Remote Sensing | |||
| Volume | 13 | |||
| Number | 13 | |||
| Pages | e2428 | |||
| Secondary Mark | B3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I | |||
| History (UTC) | 2021-07-01 19:39:49 :: simone -> administrator :: 2021-07-01 19:39:50 :: administrator -> simone :: 2021 2021-07-01 19:40:36 :: simone -> administrator :: 2021 2022-04-03 22:27:25 :: administrator -> simone :: 2021 | |||
| 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 Earth observation data data cubes satellite image time series machine learning and deep learning for remote sensing R package | |||
| Abstract | The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the worlds fast moving agricultural frontiers for the year 2018. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > SER > Satellite Image Time... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Satellite Image Time... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGP3W34T/4524AB2 | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGP3W34T/4524AB2 | |||
| Language | en | |||
| Target File | remotesensing-13-02428.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | allowpublisher allowfinaldraft | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/46KUATE | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/10.18.22.34 3 sid.inpe.br/mtc-m21/2012/07.13.14.49.22 2 | |||
| Dissemination | WEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readpermission rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||