July 14, 2021 Dataset Open Access

Sylvain Lobry; Begüm Demir; Devis Tuia

DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.5084904</identifier>
  <creators>
    <creator>
      <creatorName>Sylvain Lobry</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4738-2416</nameIdentifier>
      <affiliation>LIPADE, Université de Paris, Paris, France</affiliation>
    </creator>
    <creator>
      <creatorName>Begüm Demir</creatorName>
      <affiliation>Technische Universität Berlin</affiliation>
    </creator>
    <creator>
      <creatorName>Devis Tuia</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0374-2459</nameIdentifier>
      <affiliation>Ecole Polytechnique Fédérale de Lausanne, Sion, Switzerland</affiliation>
    </creator>
  </creators>
  <titles>
    <title>RSVQAxBEN</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Visual Question Answering</subject>
    <subject>Remote Sensing</subject>
    <subject>Sentinel 2</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-07-14</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5084904</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5083737</relatedIdentifier>
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  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Visual Question Answering is a new task that can facilitate the extraction of information from images through textual queries: it aims at answering an open-ended question formulated in natural language about a given image. In this work, we introduce a new dataset to tackle the task of visual question answering on remote sensing images: this large-scale, open access dataset extracts image/question/answer triplets from the BigEarthNet dataset.This new dataset contains close to 15 millions samples and is openly available&lt;/p&gt;</description>
    <description descriptionType="Other">With data splits</description>
  </descriptions>
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