Chen Xu’s research while affiliated with Wuhan University and other places

Spatial data infrastructures are currently being set up within regions, countries and across national border. In SDIs, the data sharing is replaced by the service sharing gradually. Many geospatial services are provided on the Internet, WPS-based remote sensing image processing is a special type in these services, it can differ widely in their functionality. Current (keyword-based) approaches to service discovery are inherently restricted by the ambiguities of natural language, which can lead to low precision and/or recall. In order to overcome this limitation, WSMO is chosen as the description framework of service, and a semantic discovery method based on logic programming is used to match the service description. In result, search results are more accurately, the semantic heterogeneity could be solved.

Along with the development of earth observation technology, large amounts of geospatial information are accessible. There are also a lot of geospatial data and services which are shared on the Internet. However they vary in formats and are stored at various organizations leading to problems of data discovery, data interoperability and usability. The Open Geospatial Consortium (OGC) has developed standard service called catalogue in order to overcome this problem. The goal of a geospatial catalogue is to support a wide range of users in discovering relevant geographic data and services from heterogeneous and distributed repositories. But in most of geospatial catalogue services, the search functionality is limited to the direct match of keywords from metadata, the OGC catalogues may not return useful results as the used keywords often do not match with the meta-information stored in the catalogues. In this paper, we propose a geospatial semantic catalogue services that aims at overcoming this limitation.

Nowadays, huge volumes of imagery and gridded data are accessible. However they vary in formats and are stored at various organizations leading to problems of data discovery, data interoperability and usability. ISO19115, Content Standard for Digital Geospatial Metadata and some other norms don't provide enough provision for imagery and gridded data. UML and XML schema of metadata also don't provide semantic description of the data content within the dataset. So we provide the additional structure to describe the derivation of imagery and gridded data. This structure is intended to augment the existing metadata standard described in ISO 19115. We focus on identification information, data quality information, spatial representation information, content information, acquisition Information in ISO 19115, we extend these dataset packages, in order to describe the metadata of imagery and gridded data during the cycle of the image product. OWL permits a much richer table of semantics as well as a more flexible definition of classes and their attributes when compared to UML and XML schema. We propose the mapping rule between UML and OWL, summarize the similarities between UML and OWL concepts. Then we explain in detail the conventions we have adopted to translate the metadata UML model for imagery and gridded data into an OWL ontology, including naming conventions, data types, and restriction conditions between the two concepts. Finally, we store imagery and gridded data metadata ontologies and individuals in a semantic registry prototype, to demonstrate how to fully utilizing the semantic information implicitly embedded in metadata.