The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W20
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W20, 97–100, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W20-97-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W20, 97–100, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W20-97-2019

  15 Nov 2019

15 Nov 2019

SEMANTIC CONTEXT-BASED ON-DEMAND SERVICE MODEL FOR LAND COVER CHANGE DETECTION

H. Q. Xing1 and D. Y. Hou2 H. Q. Xing and D. Y. Hou
  • 1School of Surveying and Geo-informatics, Shandong Jianzhu University, Jinan, China
  • 2School of Geosciences and Info Physics, Central South University, Changsha, China

Keywords: Change Detection, Semantic Context, On-demand Service, Service Composition

Abstract. 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.