Christian Igel is a professor at DIKU, the Department of Computer Science at the University of Copenhagen. He studied Computer Science at the Technical University of Dortmund, Germany. In 2002, He received his Doctoral degree from the Faculty of Technology, Bielefeld University, Germany, and in 2010 my Habilitation degree from the Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany. From 2003 to 2010, he was a Juniorprofessor for Optimization of Adaptive Systems at the Institut für Neuroinformatik, Ruhr-University Bochum. In October 2010, he was appointed professor with special duties in machine learning at DIKU.
He has been a full professor at DIKU since December 2014. Christian is also the director of the SCIENCE AI Centre and a co-lead of the Pioneer Centre for Artificial Intelligence, Denmark. He is a Fellow of ELLIS, European Lab for Learning and Intelligent Systems.
His main research interests are support vector machines and other kernel-based methods, evolution strategies for single- and multi-objective optimization and reinforcement learning, PAC-Bayesian analysis of ensemble methods, and deep neural networks and stochastic neural networks.
Deep Learning and remote sensing for ecosystem monitoring
Progress in remote sensing technology and machine learning algorithms enables scaling up the monitoring of ecosystems. This leads to new knowledge about their status and dynamics, which will be helpful in land degradation assessment (e.g., deforestation), in mitigating poverty (e.g., food security, agroforestry, wood products), and in managing climate change (e.g., carbon sequestration).
This talk will first present deep learning for the mapping of individual trees and forests. Tree crowns are segmented in satellite imagery using fully convolutional neural networks. This provides detailed measurements of the canopy area and of the distribution of trees within and outside forests. Allometric equations are applied to estimate the biomass (and thereby the stored carbon) of the individual trees. The talk will discuss some technical aspects of fitting and uncertainty quantification of allometric models.
Then it is shown how tree biomass can be directly inferred from LiDAR (laser imaging, detection, and ranging) measurements using 3D point cloud neural networks. This leads to highly accurate results without requiring a digital elevation model. In this context, we will discuss a general problem when using deep learning for least-squares regression, namely that the error residuals of the neural network do not necessarily vanish. This can lead to systematic errors that accumulate if we are interested in the total aggregated performance over many data points. We suggest addressing this issue as a default postprocessing step.