Making human place knowledge digestible by computers: Designing a place-based GIS
This project aims to deliver the fundamental computational methods to capture, model, process, and interact on human place knowledge. People understand their environment in a very different way to computers: They think of places and their relations, while computers use coordinates and maps. People’s interaction with maps is cognitively costly and error-prone, which is becoming untenable in contexts requiring time-critical decision making. The new fundamental computational methods will enable smart human-machine interaction that intuitively is based on place knowledge. The developed methods will be evaluated in scenarios such as general search, crowd-sourced data capture, or interaction with autonomous vehicles.
Leader: Stephan Winter
Staff: Stephan Winter, Tim Baldwin, Martin Tomko, Maria Vasardani
Collaborators: Jochen Renz (ANU), Werner Kuhn (UCSB)
Networks and data in society
artificial intelligence; natural language processing; spatial information