# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 1 Introduction
Regarding the data representation layer in Figure 1, many research works in the areas of Geographic Information Systems (GIS) and Spatial Data Management were dedicated to the definition of geospatial data models [6, 7]. Research solutions led to the proposal and adoption of standards, first of the Open Geospatial Consortium (OGC) and now also of ISO (see the center-left part of Figure 1). Two main types of geospatial data entities have been identified, namely, features and coverages. Figure 1: Research challenges to bridge the gap between geospatial and conventional open data infrastructures. A feature [8] is an entity of a given application domain that may be described by properties of conventional and geospatial data types [9], which include points, lines, and surfaces represented with vectors of coordinates (vector geometric representation). Examples of features with geospatial properties are cities, rivers, roads, and municipalities. A coverage [10] is a special type of feature whose conventional properties may vary along its spatial, temporal or spatio-temporal domain. An example of a spatio-temporal coverage is the variation of air temperature, wind speed, wind direction and rainfall through a given geospatial region and during a given period of time. The most common discrete representation of the domain of spatial coverages, called raster representation, is based on a regular square grid or tessellation. In addition to the above geospatial data types, metadata standards were also defined, both at the level of generic geospatial dataset level metadata (ISO 19115) and at the level of provenance environmental observation metadata [11]. Contrary to this, conventional open data infrastructures rely on semantic web data and metadata representation standards (see the center-right part of Figure 1). Despite the existence of some basic standardized solutions for the incorporation of geospatial vector data in semantic web technologies [12], and for the definition of ontologies on the environmental data management domain [13; 14], in general, the support for geospatial data representation and environmental data semantics in those technologies are still far from being satisfactory. Finally, the searching and browsing layer in Figure 1 supports data discovering and access through OGC and ISO standard web services (as it is shown in the top left part of Figure 1). Those standards enable metadata searching [15], feature and coverage data access [16; 17] and server-side map rendering [18] for geospatial visualization and browsing. On the other hand, conventional open data infrastructures rely on either simple open data formats published through specific REST APIs or data portals, or on more complex data query solutions based on the use of the SPARQL Protocol and RDF Query Language (SPARQL) [19]. In any case, despite the existence of a geospatial extension of SPARQL [12] that supports only geospatial vector feature data, in general, the complexity of the spatio-temporal structures and semantics found in environmental data sources is not supported by currently available conventional open data infrastructures. This paper reviews the state of the art related to geospatial and environmental semantic data infrastructures, which include: (i) approaches for environmental data acquisition, especially focusing on data integration problems, (ii) technologies and approaches for semantic data representation and storage and (iii) solutions for searching and browsing semantic geospatial and environmental data. Based on the characteristics of the existing solutions, the main contribution of the paper is the identification of challenges for new research activities required to bride the gap between environmental and semantic infrastructures, which would enable the construction of smart environmental data infrastructures (SEDIs). In general, the existence and appropriate management of high-quality environmental metadata is a key prerequisite to enable such new smart environmental data solutions.