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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</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-archives-XLII-4-W20-97-2019</article-id>
<title-group>
<article-title>SEMANTIC CONTEXT-BASED ON-DEMAND SERVICE MODEL FOR LAND COVER CHANGE DETECTION</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Xing</surname>
<given-names>H. Q.</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>Hou</surname>
<given-names>D. Y.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Surveying and Geo-informatics, Shandong Jianzhu University, Jinan, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>School of Geosciences and Info Physics, Central South University, Changsha, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>15</day>
<month>11</month>
<year>2019</year>
</pub-date>
<volume>XLII-4/W20</volume>
<fpage>97</fpage>
<lpage>100</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2019 H. Q. Xing</copyright-statement>
<copyright-year>2019</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-archives.copernicus.org/articles/XLII-4-W20/97/2019/isprs-archives-XLII-4-W20-97-2019.html">This article is available from https://isprs-archives.copernicus.org/articles/XLII-4-W20/97/2019/isprs-archives-XLII-4-W20-97-2019.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLII-4-W20/97/2019/isprs-archives-XLII-4-W20-97-2019.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-4-W20/97/2019/isprs-archives-XLII-4-W20-97-2019.pdf</self-uri>
<abstract>
<p>Land cover change (LCC) detection is widely used in many social-benefit areas, such as land cover updating, sustainable development and geographical situation monitoring. With the development of Web Services and cloud computing, a number of remote sensed algorithms and models have been published as web services. An on-demand service is urgent to be generated by compositing a sequence of atomic services, according to different situations. Context information plays an important role in automatic service composition. Traditional context information models mainly focus on service only, and ignore the relationships among users and services. To address this problem, we introduce the service context and user context into the context information. OWL-SC and OWL-UC are then proposed by extending the traditional service description model (i.e., OWL-S). Finally, a context-aware on-demand service model for LCC detection is built to realize service composition and optimization.</p>
</abstract>
<counts><page-count count="4"/></counts>
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