Research Projects

Self-taught learning

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 ...


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.

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 ...