Preprint Open Access

On the Semantics of Big Earth Observation Data for Land Classification

Camara, Gilberto


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3830792</identifier>
  <creators>
    <creator>
      <creatorName>Camara, Gilberto</creatorName>
      <givenName>Gilberto</givenName>
      <familyName>Camara</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3681-487X</nameIdentifier>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
    </creator>
  </creators>
  <titles>
    <title>On the Semantics of Big Earth Observation Data for Land Classification</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Big data, Earth observation, Geospatial semantics, LUCC, Land-use change</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-16</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3830792</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3830791</relatedIdentifier>
  </relatedIdentifiers>
  <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;This paper discusses the challenges of using big Earth observation data for land classification. The approach taken is to consider pure data-driven methods to be insufficient to represent continuous change. We argue for sound theories when working with big data. After revising existing classification schemes such as FAO&amp;#39;s Land Cover Classification System (LCCS), we conclude that LCCS and similar proposals cannot capture the complexity of landscape dynamics. We then investigate concepts that are being used for analyzing satellite image time series; we show these concepts to be instances of events. Therefore, for continuous monitoring of land change, event recognition needs to replace object identification as the prevailing paradigm. The paper concludes by showing how event semantics can improve data-driven methods to fulfil the potential of big data.&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
</resource>
67
44
views
downloads
All versions This version
Views 6767
Downloads 4444
Data volume 41.6 MB41.6 MB
Unique views 5757
Unique downloads 4343

Share

Cite as