The talk will summarise our efforts to densely map vegetation height over all land surfaces of the Earth. As data sources, we have employed optical imagery from ESA’s Sentinel-2 satellite and sparse waveforms recorded by NASA’s space-borne GEDI laser ranger. These observations were fused with the help of probabilistic deep learning models to obtain a canopy height map with a ground sampling distance of 10m, as well as an associated map of predictive uncertainty. Moreover, I will give a short overview over other Earth observation and environmental monitoring activities at ETH’s chair of Photogrammetry and Remote Sensing. Konrad Schindler received a Diplomingenieur (M.Tech.) degree in photogrammetry and geo-information from the Vienna University of Technology, Vienna, Austria, in 1999, and a doctorate in computer science from the Graz University of Technology, Graz, Austria, in 2003. He was a photogrammetric engineer at the private industry and held researcher positions at Graz University of Technology; Monash University (Melbourne, Australia); and ETH Zürich, Switzerland. He was an Assistant Professor of image understanding with TU Darmstadt, Darmstadt, Germany, in 2009. Since 2010, he has been a tenured Professor of photogrammetry and remote sensing with ETH Zürich. His research interests include computer vision, machine learning, remote sensing and Earth observation.
