The KnowLand project is designed to be the continuation of our previous project SCALA (Scales and Hierarchies in Landform Classification), which ran between mid 2008 and mid 2011.

The overall objective of this follow-up project is to develop a sound methodological framework for the integration of semantics in hierarchical landform modelling based on our previous research on detecting and analysing characteristic land-surface patterns in the object based image analysis (OBIA) realm. Automated classification/extraction of features in OBIA requires the matching of computed image objects with concepts of real-world objects by making use of models that capture the meaning of the features of interest in a structured way. The main objectives of the methodology are:

1. To formalize existing knowledge about the morphology, morphometry and the context of glacial landforms by semantic modelling;

2. To match the semantic landform model with significant object patterns of land-surface models as well as with useful object and pattern properties in order to develop transferable rules for landform classification;

3. To investigate ways for assessing the stability and transferability of derived landform classes across spatial resolutions as well as across areas with comparable topography.

The proposed framework will add to increased objectivity and transferability of OBIA-based landform classification. In the broader sense expected outcomes shall contribute to laying the scientific foundations for semantics-based extraction of any kind of features in OBIA from multidimensional and multiscalar field representations such as digital elevation models (DEMs).

Project duration

Aug 2011 - March 2013 (20 months)

 

Funding

FWF Austrian Science Fund