Minimize Optimisation of a Landform Analysis Tool

Date added: 21 January 2014

Organisation: DLR

Location: Oberpfaffenhofen, Germany


The Remote Sensing Technology Institute of DLR has an available vacancy for a student in geodesy, geography or a similar field to optimise a topographical algorithm.

In recent years various approaches for classifying digital surface models were developed. One of these algorithms is the so called topographic position index. The results of this algorithm are used for describing and explaining different real world phenomena, like the movement of wildlife or the position of archaeological sites. In order to archive good classification results, different parameters and extensions have to be taken into account, for example:

  • Topography of area of interest
  • Geometric resolution of digital surface model
  • Different radii for kernel map
  • Extension of algorithm with slope angles

The goal of this thesis is to analyse and extend an existing algorithm. The result of the classification shall determine regions, which are strongly endangered by extreme rains and wind speeds due to their spatial exposition. Statistical analysis of the classification results has to be done, using different data sources and data types. Programming work has to be done in Python.


  • Get an overview of the classification algorithm and the parameters used
  • Search and analyse multiple data sources for the specific task, like meteorological data, geomorphological data, and evaluate the results of the algorithm with these data
  • Create a classified terrain map, which shows endangered regions, based on statistical analysis