Visualization of EO data

For the past years, we are experiencing an increase in the variety of Earth Observation data that are becoming available as Linked Data. This leads to an increased number of non-expert users that are expected to analyze these data. Visualization techniques provide inexperienced users an intuitive way to explore and analyze Linked Data, but also assist expert users with new perspectives. The tool Sextant was developed to address the need of a web-based GIS system that could handle linked geospatial data, but also offer the ability to interact with the most common GIS formats. Sextant supports the visualization of queries using the languages GeoSPARQL and stSPARQL, and also supports the visualization of the most common GIS formats, as layers on a map. The tool also allows the creation of thematic maps by combining different layers, and stores these maps are web resources, following the RDF paradigm.

Sextant will allow the visualization of the DeepCube platform data that hold geospatial information and allow the interlinking in map level with different sources to assist the use cases and create thematic maps that would be distributed as web resources. Moreover, the intuitive query builder that will be developed, will utilize the new ontologies that will accompany the semantic data cube. Non-expert users will be able to use this feature to explore the different classes available in the data and create filters over their properties. These visualization features will assist in the creation of user interfaces for the use cases, but also allow expert and non- expert users to explore, analyze and visualize the information that will be available in the semantic data cube.

Sextant is a web based and mobile ready platform for visualizing, exploring and interacting with linked geospatial data. Sextant is a user-friendly application that allows both domain experts and non-experts to take advantage of semantic web technologies, creating thematic maps by combining spatio-temporal information with other data sources, e.g. industrial intelligence, socio-economic data, etc., allowing visual analytics based on big Copernicus data.

Interested in learning more? Contact us!
Manolis Koubarakis,
George Stamoulis,