# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 7 Conclusions  
Environmental datasets, generated from observation and modeling processes, are important sources of highly valuable information for decision making in many application domains. Those applications include different domains of scientific research, environmental and infrastructure management, agriculture, and other applications more directly in contact with citizens such as smart cities and touristic and leisure activities. However, and in spite of some attempts to apply semantic technologies in the geospatial and environmental domain, environmental data discovering and access infrastructures are still targeted to experts in the areas of GIS and environmental data management. Due to this, in general, IT practitioners are either not aware of the existence of these infrastructures or do not have the required skills to get the required profit from them. As a consequence, many opportunities to reuse these expensive and high-quality datasets in many other application domains and to open new interesting business models from them are lost.  
SEDIs are proposed in this paper as the solution to the above problems. Their design and implementation will be based on the effective extension of semantic web technologies with geospatial data management capabilities. Research challenges to achieve the above objective focus mainly in (i) the incorporation of raster data in semantic representations and ontologies, (ii) the automatic discovering of data integration knowledge and the effective fusion of vector and raster datasets, (iii) the efficient federated querying of vector and raster datasets, (iv) the effective and efficient semantic keyword-based search and (v) the implementation of efficient exploratory analysis.  
state of the art supervision, challenge identification and paper writing, J.R.R.V.; state of the art review S.V. and D.M.; state of the art supervision and writing review, J.A.T. All authors have read and agreed to the published version of the manuscript.  
This research was co-funded by (i) the TRAFAIR project (2017-EU-IA-0167), co-financed by the Connecting Europe Facility of the European Union, (ii) the RADAR-ON-RAIA project (0461_RADAR_ON_RAIA_1_E) co-financed by the European Regional Development Fund (ERDF) through the Irrepe V-A Spain-Portugal program (POCTEP) 2014-2020, and (iii) the Conselleria de Educacion, Universidade e Formacion Professional of the regional government of Galicia (Spain), through the support for research groups with growth potential (ED431B 2018/28).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
API Application programming interface  
CDM Common data model  
CF Climate and forecast  
CUAHSI Consortium of Universities for the Advanced of Hydrologic Science  
CSW Catalog Service  
DBMS Database management system  
DAB Discovery and access broker  
DCAT Data Catalog Vocabulary  
DCAT-AP DCAT application profile  
DQV Data Quality Vocabulary  
ETL Extract transform and load  
GEOSS Global Earth Observation System of Systems  
GIS Geographic information systems  
GML Geography Markup Language  
HIS Hidrological information system  
HTTP Hypertext Transfer Protocol  
INSPIRE Infrastructure for Spatial Information in Europe  
IoT Internet of things  
ISO International Organization for Standardization  
IT Information technology  
JSON JavaScript object notation  
MQ telemetry transport  
NCSS NetCDF subset service  
NCSI Next generation service interface  
OASIS Organization for the Advancement of Structured Information Standards  
ODM Observations data model  
OCC Open Geospatial Consortium  
OLAP  
Ohline analytical processing  
O&M Observations and measurements  
O-MI Open messaging interface