RN (Robert) Masolele MSc

RN (Robert) Masolele MSc


Robert is a Postdoctoral Researcher at the Laboratory of Geo-information Science and Remote sensing of Wageningen University. His work exploits the advancement in earth observation and artificial intelligence (AI) to enable the detection of spatial patterns in environmental data based on satellite images. The detection of varying spatial patterns is critical for (1) land use characterization at small scale and larger scales, (2) time series land use change dynamics assessment, and (3) monitoring of proximal drivers of land use change associated with forest carbon emissions. The use of artificial intelligence is rather essential for the automated assessment of the interacting and complex land use types (natural forest vs plantation forest, Commercial agriculture vs Small scale agriculture Vs Pasture). Apart from spatial land use pattern characterization. He also incorporates a time series stack of Sentinel -1, Sentinel -2, and Landsat images into AI algorithms for time series analysis. The algorithms are trained using visually interpreted land use patches.

Robert holds a Ph.D. in remote sensing and deep learning. His Ph.D. research work focused on using artificial intelligence algorithms to assess land use change on national and sub-national scales with remote sensing time series. The Ph.D. was part of the  CIFOR's global comparative study on REDD + (GCS).

Robert also holds a Master of Science degree in Geo-information Science and Earth Observation from the Faculty of Geo-information Science and Earth Observation of the  University of Twente with a research specialization in using remote sensing data for aboveground biomass and carbon stock assessment. He has +3 years of experience working in forest resource assessment and conservation for the Tanzanian Forest Service.

He enjoys carefully thinking about the highly mathematical parts of analyzing natural resource problems, particularly land use characterization and land use dynamics assessment, with the application of spatial statistical modeling, machine learning, and deep learning algorithms.

For further information on his topic, you can check him on Twitter and LinkedIn.