Question Answering (QA), the process of computing valid answers to questions formulated
in natural language, has recently gained attention in both industry and academia …

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist
should know before they start working with spatial data. These aspects include how …

Geographic data is growing in size and variety, which calls for big data management tools
and analysis methods. To efficiently integrate information from high dimensional data, this …

Earth observation (EO) datasets are commonly provided as collection of scenes, where
individual scenes represent a temporal snapshot and cover a particular region on the Earth's …

Loose programming enables analysts to program with concepts instead of procedural code.
Data transformations are left underspecified, leaving out procedural details and exploiting …

Provenance, a metadata component referring to the origin and the processes undertaken to
obtain a specific geographic digital feature or product, is crucial to evaluate the quality of …

In this article, we critically examine the role of semantic technology in data driven analysis.
We explain why learning from data is more than just analyzing data, including also a number …

In geographic information systems (GIS), analysts answer questions by designing workflows
that transform a certain type of data into a certain type of goal. Semantic data types help …

The increasing availability of geospatial data offers great opportunities for advancing
scientific discovery and practices in society. Effective and efficient processing of geospatial …

In Geographic Information Systems (GIS), geoprocessing workflows allow analysts to
organize their methods on spatial data in complex chains. We propose a method for …