Beyond SDI – Mariana Belgiu (ITC)
Spatial Data Infrastructures (SDIs) have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on demand computational and storage capacities over the Internet. As a consequence of these efforts and technological developments, an increasing number of user-friendly online geoportals have been developed. Thus, users can easily search, find and use data shared across different online platforms.
It is time to go a step further and start sharing workflows, algorithms and codes that allow users to convert the vast quantities of SDI data into purpose-driven information. Mainstream data analysis methods such as machine learning can be used to transform these data into useful information on demand. Thus, the focus should be on developing operational workflows to convert big data stored in SDIs into information. This information can be further used to address societal challenges including climate change, poverty or food security that are high on the agenda of international initiatives such as United Nations’ Sustainable Development Goals (SDGs). To that end, the availability of automated workflows would leverage the current SDIs by transforming them from merely data pools into data analysis platforms serving various societal needs.