Research Projects


Björn Waske

Jan 01, 2018 — Dec 31, 2018

The main objective of the project is the development of an e-learning course in remote sensing data anyslsis, among others to support our regular remote sensing classes by a blended learning concept. Moreover the e-learning course will demonstrate typical, practically oriented workflows - including data acquisition and preprocessing, image analysis, and accuracy assessment - to foster and support the use of remote sensing imagery in the next data science project or final thesis. Basic and ...


Björn Waske

Jan 01, 2018 — Dec 31, 2018

The project aims on the development of an e-learning course for geostatistical data analysis in R. The project is conducted with Dr. Kai Hartmann, Applied Geography, Environmental Hydrology and Resource Management, FU Berlin

Completed Projects

List of completed research projects.

Research Projects — finished


Prof. Dr. B. Waske

Nov 01, 2014 — Oct 31, 2017

Although the use of remote sensing data for urban areas was demonstrated in various studies, mapping urban land use and land cover is challenging. Among others the spectral heterogeneity in urban imagery is very high and spectrally similar materials might occur on different surface types. Besides spectral ambiguities, high frequent spatial patterns of urban land cover categories result in a relatively high number of mixed pixels. Behind these facts common multispectral sensors can be limited to ...

Self-taught learning

Björn Waske

Aug 01, 2013 — Jul 31, 2016

Earth Observation data play a major role in supporting decision-support systems and monitoring compliance of several multilateral environmental treaties. Land cover maps of remote sensing data are the most commonly used product in this context and the development of feasible and accurate classification strategies is an ongoing research field. Particularly the classification of larger areas is often challenging, e.g., due to the lack of adequate amount of training and validation data. This ...


Björn Waske

May 01, 2013 — Apr 30, 2016

SenseCarbon develops methods to improve the mapping of REDD+ relevant land use and land cover change processes. In preparation of the upcoming Sentinel missions, SenseCarbon uses existing optical and SAR remote sensing data archives of different spatial and temporal resolutions.