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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CTA2S
Repositorysid.inpe.br/sibgrapi/2021/09.06.13.34
Last Update2021:09.06.13.34.59 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.06.13.34.59
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyMachadoNoguSant:2021:ScClUs
TitleScene classification using a combination of aerial and ground images
FormatOn-line
Year2021
Access Date2024, Apr. 28
Number of Files1
Size4071 KiB
2. Context
Author1 Machado, Gabriel Lucas Silva
2 Nogueira, Keiller
3 dos Santos, Jefersson Alex
Affiliation1 Universidade Federal de Minas Gerais
2 University of Stirling
3 Universidade Federal de Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressgabriel.lucas@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2021-09-06 13:34:59 :: gabriel.lucas@dcc.ufmg.br -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsdeep learning
machine learning
remote sensing
image classification
multi-modal machine learning
metric learning
cross-view matching
Abstractlt is undeniable that aerial images can provide useful information for a large variety of tasks, such as disaster relief, and urban planning. But, since these images only see the Earth from one point of view, some applications may benefit from complementary information provided by other perspective views of the scene, such as ground-level images. Despite a large number of public image repositories for both georeferenced photos and aerial images (such as Google Maps, and Street View), there is a lack of public datasets that allow studies that exploit the complementarity of aerial+ground imagery. Given this, we present two new publicly available datasets named AiRound and CV-BrCT. Using both, we tackled the scene classification task in 2 different scenarios. The first one has a fully-paired image set, while the second has missing samples. In both situations, we used deep learning and feature fusion algorithms. To handle missing samples, we proposed a content-based image retrieval framework.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Scene classification using...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CTA2S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CTA2S
Languageen
Target FileWTD_Gabriel.pdf
User Groupgabriel.lucas@dcc.ufmg.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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