The hypothesis of this work is that objects (like forests or water bodies) are not needed for most change models and can be substituted by observations, surfaces, substances, and media. Avoiding objects as long as possible in the “food chain of sensors” has the advantage of reducing the semantic heterogeneity of models, as object classes vary across information communities. The MSc student will study at least two change questions (e.g., deforestation, glacier melt), work out the chain of sensor abstractions leading to answers, and show at what level object notions need to be introduced, if any.

First examiner and point of contact: Werner Kuhn (kuhn@uni-muenster.de) Advisors: Jens Ortmann and/or Anu Devaraju Mobility measure: optional (for the use cases)