• Prof. Ribana Roscher, Professor in Remote Sensing, Remote Sensing Group, Institute of Geodesy and Geoinformation, University of Bonn. Website: http://rs.ipb.uni-bonn.de/people/prof-dr-ing-ribana-roscher/

Tilte: Explain the world to me - On the benefit of explainable machine learning for Earth observation

Abstract: Machine learning methods have been increasingly used in scientific disciplines for some time. In addition to high accuracy, one desired goal is to learn explainable models and understand how a particular decision was made. To achieve this goal and obtain explanations, knowledge from the domain is needed that can be integrated into the model or applied post-hoc. This talk will focus on detecting wilderness characteristics with explainable machine learning and show that machine learning can not only be used to learn models that should align with our existing knowledge but can also lead to new insights.

• Prof. Jan Dirk Wegner, Professor, Institute of Geodesy and Photogrammetry, University of Zurich. Website: https://igp.ethz.ch/personen/person-detail.html?persid=186562

Title: Large-scale analysis of geospatial data with machine learning

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.