# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 6 Wrap-up Discussion on New Research Challenges
#### 2.2.3 Challenge 4: Geospatial to RDF Mappings  
The first step towards data integration is to achieve syntactic homogeneity among all the datasets within the same data representation framework. Therefore, to implement SEDIs, appropriate mappings are required from existing geospatial data models to the geospatial RDF representation defined in challenge 1. Those mappings should support both vector feature and raster coverage sources and,in a way similar to that of the relational to RDF mappings, they should include both direct [77] and user defined [78] mappings. In particular, relational to RDF mappings should be extended to have full support of vector feature sources, and to add raster coverage support. **Challenge 5**: Geospatial Data Integration Knowledge Discovery  
Once syntactic integration is achieved, semantic conflicts between the data sources have to be identified and solved. To achieve this, first a global data model is generally assumed. The characteristics of such a model has already been described as part of challenge 3. Next, data integration knowledge that maps local concepts of each data source to global concepts, has to be generated. Currently available solutions assume in general that such knowledge is already available [45; 51; 87; 88; 89; 90; 91]. However, defining such knowledge is a tedious process that requires the implication of application domain experts. Therefore, automatic and semi-automatic data integration knowledge extraction mechanisms should be devised to alleviate the expert's work. Those knowledge extraction processes will leverage already existing automatic ontology mapping approaches [96], which should be extended to incorporate the geospatial dimension of the data and metadata, to achieve better effectiveness (precision and recall) in the knowledge discovery process and more usable graphical user interfaces for the interaction with the application domain experts. **Challenge 6**: Smart Geospatial Fusion of Heterogeneous Vector-Raster Data  
Once local and global data structures have been matched, both syntactically and semantically, data element level integration, i.e., data fusion must be undertaken. Ad-hoc approaches that combine different types of environmental data, including text and geospatial formats [94], and observation and model data [95], have already been implemented. Those ad-hoc approaches obtain, in general, integrated datasets with an average error that is lower than that of the input datasets. Ad-hoc solutions may be designed to combine vector and raster datasets. However, every new data fusion task requires a new algorithm to be designed and implemented, needing the participation of both data science and application domain experts. Generalizing those ad-hoc approaches maintaining at the same time the performance in terms of error is the main objective of this challenge. The general data fusion problem has been formalized for vector features [93], and it includes dataset concatenation with possible duplicate elimination [97], data accumulation, feature correction, feature enrichment, and feature update and difference. However, the solution does not consider raster sources and of course, the performance in terms of error may still be improved. **Challenge 7**: Semantic Federated Querying over Vector and Raster Datasets  
Geospatial data querying functionality is needed to implement data searching and browsing. Additionally, federated querying may also be of great importance during the implementation of Extract Transform and Load (ETL) processes in the data integration stage, after the RDF mappings of Challenge 4 have been applied. Both vector and raster data sources should be supported by a semantic geospatial federated query engine. To achieve and efficient implementation in terms of query response time, it is also very important to take into account the data processing capabilities of the data sources during the construction of the federated query plans, trying to place geospatial processing as close to the data as it is possible.