Geospatial data harmonization from regional level to european level: a usa case in forest fire data

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Geospatial data harmonization is becoming more and more important to increase interoperability of heterogeneous data derived from various sources in spatial data infrastructures. To address this harmonization issue we present the current status of data availability among different communities, languages, and administrative scales from regional to national and European levels. With a use case in forest data models in Europe, interoperability of burned area data derived from Europe and Valencia Community in Spain were tested and analyzed on the syntactic, schematic and semantic level. We suggest approaches for achieving a higher chance of data interoperability to guide forest domain experts in forest fire analysis. For testing syntactic interoperability, a common platform in the context of formats and web services was examined. We found that establishing OGC standard web services in a combination with GIS software applications that support various formats and web services can increase the chance of achieving syntactic interoperability between multiple geospatial data derived from different sources. For testing schematic and semantic interoperability, the ontology-based schema mapping approach was taken to transform a regional data model to a European data model on the conceptual level. The Feature Manipulation Engine enabled various types of data transformation from source to target attributes to achieve schematic interoperability. Ontological modelling in Protégé helped identify a common concept between the source and target data models, especially in cases where matching attributes were not found at the schematic level. Establishment of the domain ontology was explored to reach common ground between application ontologies and achieve a higher level of semantic interoperability

Ontology-Driven Translation of Geospatial Data

Current methods for specifying data models lack well-defined descriptions of the expressions, which represent data types, attributes, attribute values, and operations. It is impossible to define clear translation rules for data models, because the relation between source and target data model elements is not computable. Furthermore, translation has to account for imprecision caused by conceptual heterogeneities and measurement error. In this thesis we use the DOLCE foundational ontology to provide a semantic reference frame for geospatial data. Available extensions to DOLCE are profiled and additional geospatial characteristics, such as topological relations are included. Annotating (or semantically referencing) expressions, which are used to define geospatial data models, with this frame supports computability and allows for selecting appropriate translation rules on the attribute level. Using a logic-based approach, semantically referenced data models allow for inferring relations between source and target attributes. This includes inference on applicable translation operations and the detection of match types (exact, upper bound, and lower bound) between attributes. If detected match types fit the user¿s purpose, translation scripts are extracted. The scripts are executed using an algebraic theory, which includes propagation of measurement errors. The approach allows for specifying data model semantics and imprecision. A demonstrator is provided as proof of concept. Our research is guided by an example of translating information about road width from a national data model (ATKIS road data model) to an international one (INSPIRE Data Specification for Transport Networks).JRC.DDG.H.6-Spatial data infrastructure