BSc Hon: Cartography and Geomedia, Germany
BSc: Applied Computer Science, Major: Business Information Systems, Germany
- Find solutions for managing and processing of big data
- Develop methodologies for better decision making in real world applications
- Find hidden patters and unknown relationship in satellite imagery which will give a better insight of the underlying data structure
Title: Prediction of structured noisy data using boosted regression trees
Spatio-temporal time series analyses are important to get more in-depth knowledge in processes from the past. We can learn from observing the past for the future. An estimation of future vegetation cover is based on the knowledge from the past in order to understand triggers of change in the vegetation and its impacts. Since the vegetation cover is a dynamic process with a high variation due to climate variability it is important to understand where (as a spatial question) did happen what (as a spatio-temporal question and why (as a spectral question which are showing individual grey values indicating the reflectance). Therefore, climate variability and resulting land use change monitored through a time series need to be accounted for this investigation.
One challenge will be to combine all data sources together, as well as address the spatial, spectral, spatio-temporal aspects and other provided data. This project will attempt to develop statistical machine learning models (boosted regression trees) to meet these challenges.
After finishing my studies in Cartography and Geomedia, I gathered work experience as a GIS Analyst for 8 years. Since the GIS field changed rapidly to a more computational related discipline I decided to study Computer Science in addition my existing spatial degree to make sure to stay on top. The combination of those two major fields is a very promising one and I see a lot of potential in designing new methodologies which will assist in the decision making process for real world applications.