In an Ambient Intelligence world, we should always have the help we need to seamlessly navigate the physical world. We share best practices on handling geospatial data and, over time, develop solutions for the challenges encountered with this data along the chain of data collection, fusion, inference and visualization which have not been addressed by the state of the art. Our main research questions are around the representation of real world physical artefacts (people, places, objects and activities) with this data i.e. How can one build a single representation of these artefacts from various sources of observation which are in nature partial and approximate? How can they be represented and visualized beyond the map in a way that captures their dynamic nature whether its past, real time or future? How can we connect the representations of these artefacts with how they are perceived by people?

Our expertise in geospatial data originates from the many years we worked on transportation and mobility (mobility patterns analytics from big data, multimodal trip planning). Whilst mobility remains a major application in our group, geospatial data has become a more fundamental concern since spatiality of data is a core dimension in an Ambient Intelligent environment.