# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 4 Data Acquisition and Integration
### Smart Environmental Data Integration
All the above approaches rely on the existence of data integration knowledge in the form of mappings between local and global ontologies, which has to be provided by experts of the application domains. The above semantic data integration solutions focus on the resolution of semantic conflicts between data source metadata during global data access. However, other challenges related to the effective fusion of various data sources that refer to the same variables have to be faced, to achieve more complete datasets that are also more precise. Those challenges include issues related to data imperfection, data correlation, data inconsistency and data disparateness [92]. The data fusion problem in the scope of geospatial data sources has already been formally specified [93]. The spatial attributes of geospatial features from various datasets have to be appropriately combined to achieve data concatenation with possible duplicate elimination, geometric and/or temporal correction, feature enrichment and feature update and difference. It is noticed that the above approach restricts to only vector features. A specific solution that combines meteorological and air quality data has been described in [94]. The data sources include numerical environmental data sources and also text documents extracted from the web. The fusion algorithm is based on a statistical approach and it uses expert information to determine source reliability and to transform linguistic values to numeric values. A similar approach is the fusion of observed and modeled air quality data undertaken in [95] for the generation of real-time interpolated data. Notice that the above solutions are application specific, thus, to the best of these authors' knowledge, general purpose solutions for data fusion between vector features and raster coverages in the environmental data management context are not available. In summary, much work has been done related to the semantic integration of environmental metadata using ontologies, however, the approaches available so far rely on the existence of data integration knowledge provided by experts. Regarding data fusion, the problem has been formalized for vector features, but only application-specific solutions exist with raster coverages of environmental data. Based on the above the two following challenging problems are identified in this area (see Figure 9). **Challenge 4: geospatial to RDF mappings. **: To support the syntactic integration of geospatial data sources into the semantic data representation storage technologies described in the previous section, mapping solutions similar to those between the relational model and RDF [77; 78] have to be developed between geospatial data models and RDF. At a first glance, it seems that mappings between relational data and RDF may directly be applied to vector feature datasets, however, this is not the case of geospatial coverages, whose underlying data model is not based on entities. **Challenge 5: geospatial data integration knowledge discovery. **: Data integration knowledge has the form of semantic relationships between classes of local and global ontologies. Most commonly, those relationships define class hierarchies and equivalences. The automatic discovering of these kinds of relationships is the main objective of ontology mapping solutions [96]. They may either define mappings between a global ontology and various local ontologies, or directly between local ontologies. The key issue in this challenge is to take advantage of both the special characteristics of geospatial features and coverages and the already existing environmental data models and ontologies to improve the effectiveness of the solutions. **Challenge 6: smart geospatial fusion of heterogeneous vector-raster data. **: The data integration knowledge discovered by the approaches of the previous challenge, enables the identification of which geospatial vector features and raster coverages may be candidates to undergo a further data fusion process.