Abstract

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 Z ürich, 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 Z ürich. His research interests include computer vision, machine learning, remote sensing and Earth observation.