September 16, 2019 Conference paper Open Access

Zhang, Rui; Freitag, Marcus; Albrecht, Conrad; Zhang, Wei; Lu, Siyuan

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  <identifier identifierType="DOI">10.5281/zenodo.3387715</identifier>
  <creators>
    <creator>
      <creatorName>Zhang, Rui</creatorName>
      <givenName>Rui</givenName>
      <familyName>Zhang</familyName>
      <affiliation>Data Intensive Physical Analytics, TJ Watson Research Center, IBM Research, Yorktown Heights, NY, United States</affiliation>
    </creator>
    <creator>
      <creatorName>Freitag, Marcus</creatorName>
      <givenName>Marcus</givenName>
      <familyName>Freitag</familyName>
      <affiliation>Data Intensive Physical Analytics, TJ Watson Research Center, IBM Research, Yorktown Heights, NY, United States</affiliation>
    </creator>
    <creator>
      <creatorName>Albrecht, Conrad</creatorName>
      <givenName>Conrad</givenName>
      <familyName>Albrecht</familyName>
      <affiliation>Data Intensive Physical Analytics, TJ Watson Research Center, IBM Research, Yorktown Heights, NY, United States</affiliation>
    </creator>
    <creator>
      <creatorName>Zhang, Wei</creatorName>
      <givenName>Wei</givenName>
      <familyName>Zhang</familyName>
      <affiliation>Data Intensive Physical Analytics, TJ Watson Research Center, IBM Research, Yorktown Heights, NY, United States</affiliation>
    </creator>
    <creator>
      <creatorName>Lu, Siyuan</creatorName>
      <givenName>Siyuan</givenName>
      <familyName>Lu</familyName>
      <affiliation>Data Intensive Physical Analytics, TJ Watson Research Center, IBM Research, Yorktown Heights, NY, United States</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Towards Scalable Geospatial Remote Sensing for Efficient OSM Labeling</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>OpenStreetMap</subject>
    <subject>GIScience</subject>
    <subject>Remote Sensing</subject>
    <subject>Big Data Analytics</subject>
    <subject>Machine learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-09-16</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
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    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3387715</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3387714</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/sotm-2019</relatedIdentifier>
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  <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;Zhang et al. (2019). Towards Scalable Geospatial Remote Sensing for Efficient OSM Labeling&lt;/p&gt;

&lt;p&gt;In: Minghini, M., Grinberger, A.Y., Juh&amp;aacute;sz, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 27-28. Heidelberg, Germany, September 21-23, 2019. Available at &lt;a href="https://zenodo.org/communities/sotm-2019"&gt;https://zenodo.org/communities/sotm-2019&lt;/a&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;DOI: &lt;a href="http://doi.org/10.5281/zenodo.3387715"&gt;10.5281/zenodo.3387715&lt;/a&gt;&lt;/p&gt;</description>
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