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On the Semantics of Big Earth Observation Data (talk)

Camara, Gilberto

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  <identifier identifierType="DOI">10.5281/zenodo.3873143</identifier>
      <creatorName>Camara, Gilberto</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-3681-487X</nameIdentifier>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
    <title>On the Semantics of Big Earth Observation Data (talk)</title>
    <subject>Big earth observation data, Land use science, Satellite image time series, Crop expansion, Brazilian Amazonia biome, Brazilian Cerrado biome,Tropical deforestation</subject>
    <date dateType="Issued">2020-06-02</date>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3873142</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Satellite images are the most extensive source of data about our environment; they provide essential information about global challenges. Since petabytes of imagery are accessible, researchers can now track changes continuously. To work with big Earth observation data, scientists are developing &lt;em&gt;data-driven and theory-limited&lt;/em&gt; methods. However, numbers do not speak for themselves. Data-driven approaches without robust theories can lead to results that will not advance our knowledge. We need sound theories to deal with big data without drowning in it.&lt;/p&gt;

&lt;p&gt;In this talk, we argue that current ontologies and descriptive schemas used in image analysis cannot capture the complexity of landscape dynamics unveiled by big data. These schemas lack expressive power. Existing ontologies for land classification are object-centered; to work with big data, we need to include occurrents. For continuous monitoring of land change, &lt;em&gt;event recognition&lt;/em&gt; needs to replace &lt;em&gt;object identification&lt;/em&gt; as the prevailing paradigm. The presentation explains how &lt;em&gt;event semantics&lt;/em&gt; can improve data-driven methods to fulfill the potential of big data.&lt;/p&gt;</description>
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