Large-scale analysis of geospatial data with machine learning

Date: Tuesday, 24 May, 17:00-18:00
Speaker: Prof. Jan Dirk Wegner
Location: Join via in HG D7.2 or via external page Zoom

Speaker Bio

Jan Dirk Wegner holds the "Data Science for Sciences" chair at the Institute for Computational Science, University of Zurich, as an Associate Professor and is head of the EcoVision Lab at ETH Zurich. Jan was PostDoc (2012-2016) and senior scientist (2017-2020) in the Photogrammetry and Remote Sensing group at ETH Zurich after completing his PhD (with distinction) at Leibniz Universität Hannover in 2011. His main research interests are at the frontier of machine learning, computer vision, and remote sensing to solve scientific questions in the environmental sciences and geosciences. Jan was granted multiple awards, among others an ETH Postdoctoral fellowship and the science award of the German Geodetic Commission. He was selected for the WEF Young Scientist Class 2020 as one of the 25 best researchers world-wide under the age of 40 committed to integrating scientific knowledge into society for the public good. Jan is vice-president of ISPRS Technical Commission II, chair of ISPRS II/WG 6 "Large-scale machine learning for geospatial data analysis", director of the PhD graduate school "Data Science" at University of Zurich, and his professorship is part of the Digital Society Initiative at University of Zurich. Together with colleagues, Jan is chairing the CVPR EarthVision workshops.

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Abstract

Worldwide analyzes and estimates of vegetation parameters such as biomass or vegetation height are essential for modeling climate change and biodiversity. Traditional allometric approaches usually have to be adapted for specific ecosystems and regions.
It is therefore very difficult to carry out homogeneous, global modeling with high spatial and temporal resolution and, at the same time, good accuracy. Data-driven approaches, especially modern deep learning methods, promise great potential here. In this talk, new research results on the large-scale determination of vegetation parameters will be presented.