Abstract
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.
Highlights
Land cover change (LCC) detection is the process of identifying land change areas and types by using remote sensed images acquired in different time (HUSSAIN et al, 2013; Tewkesbury et al, 2015)
Service context for service composition refers to the relationship between a service and other services in service composition and its scope of application, which is can be represented as Service Relation (SR) and Service Application (SA)
LCC detection services contain a variety of contextual information, which play an important role in on-demand service composition
Summary
Land cover change (LCC) detection is the process of identifying land change areas and types by using remote sensed images acquired in different time (HUSSAIN et al, 2013; Tewkesbury et al, 2015). A large number of LCC detection algorithms or models have been proposed to address land cover change requirements. In order to address such complicated geoprocessing requirement, automatic web service composition is an effective way to create service chain by composing a collection of atomic services (Cruz et al, 2012). It is can be seen as the process of searching the appropriate services based on users’ requirement (i.e., input and output data). To address the above challenges, we propose a novel approach of semantic context-based on-demand LCC detection service model. A context-aware on-demand service model is proposed to composite services to an on-demand service chain based on semantic context description and matching
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