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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Annals</journal-id>
<journal-title-group>
<journal-title>ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Annals</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9050</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-annals-VI-4-W2-2020-135-2020</article-id>
<title-group>
<article-title>INTEGRATION OF HETEROGENEOUS CORONAVIRUS DISEASE COVID-19 DATA SOURCES USING OGC SENSORTHINGS API</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Santhanavanich</surname>
<given-names>T.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Kim</surname>
<given-names>C.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Coors</surname>
<given-names>V.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>University of Applied Sciences Stuttgart, Schellingstraße 24, 70174 Stuttgart, Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>15</day>
<month>09</month>
<year>2020</year>
</pub-date>
<volume>VI-4/W2-2020</volume>
<fpage>135</fpage>
<lpage>141</lpage>
<permissions>
<copyright-statement>Copyright: © 2020 T. Santhanavanich et al.</copyright-statement>
<copyright-year>2020</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/isprs-annals-VI-4-W2-2020-135-2020.html">This article is available from https://isprs-annals.copernicus.org/articles/isprs-annals-VI-4-W2-2020-135-2020.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/isprs-annals-VI-4-W2-2020-135-2020.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/isprs-annals-VI-4-W2-2020-135-2020.pdf</self-uri>
<abstract>
<p>The latest coronavirus (namely severe acute respiratory syndrome coronavirus 2 or COVID-19) was first detected in Wuhan, China, and spread throughout the world since December 2019. To tackle this pandemic, we need a tool to trace and predict trends of COVID-19 at global, national, and regional levels rapidly. Several organizations around the world offer access to COVID-19 related data. However, these data sources are heterogeneous in terms of data formats and protocols as different organizations developed them. To address this issue, a standard way to handle these datasets is needed. In this paper, we propose using the OGC SensorThings API to manage the COVID-19 dataset in a standard form and provide access to the general public. As a proof-of-concept, we implemented a COVID-19 data management platform based on the OGC SensorThings standard named COVID-19 SensorThings or in short COVID-STA. For a use case, we developed a real-time interactive web-based dashboard illustrating the COVID-19 dataset based on the COVID-STA. As a result, we proved that the OGC SensorThings API is suitable to use as a general standard for integrating the heterogeneous COVID-19 data.</p>
</abstract>
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