Preprint Open Access

On the Semantics of Big Earth Observation Data for Land Classification

Camara, Gilberto


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Big data, Earth observation, Geospatial semantics, LUCC, Land-use change</subfield>
  </datafield>
  <controlfield tag="005">20200516202026.0</controlfield>
  <controlfield tag="001">3830792</controlfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">945052</subfield>
    <subfield code="z">md5:cba8d7005d169342f806f5fca6bef743</subfield>
    <subfield code="u">https://zenodo.org/record/3830792/files/main.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-05-16</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o">oai:zenodo.org:3830792</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="v">under review</subfield>
    <subfield code="p">Journal of Spatial Information Science</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">INPE (National Institute for Space Research), Brazil</subfield>
    <subfield code="0">(orcid)0000-0002-3681-487X</subfield>
    <subfield code="a">Camara, Gilberto</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">On the Semantics of Big Earth Observation Data for Land Classification</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3830791</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3830792</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">preprint</subfield>
  </datafield>
</record>
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