Special Lecture: Machine learning for geospatial data

Lecture on November 6, 2020.

Special Lecture: Machine learning for geospatial data

Summary. A multitude of different sensors is capturing massive amounts of geo-coded data with different spatial resolution, temporal frequency, viewpoint, and quality every day. Modelling functional relationships for applications is often hard and loses predictive power due to the high variance in sensor modality. Data-driven approaches, especially modern deep learning, come to the rescue and learn expressive models directly from (labeled) input data. In this talk, I will present probabilistic approaches as well as deep learning methods to analyze geospatial data at large scale for applications in the environmental and geosciences with emphasis on ground-level imagery, remote sensing, crowd-sourced data, and point clouds.

About the Speaker

 

Jan Dirk Wegner is head of the EcoVision Lab (9 PhDs and 3 PostDocs), which does research at the frontier of machine learning and computer vision to solve ecological questions. Jan joined the Photogrammetry and Remote Sensing group at ETH in 2012 after completing his PhD (with distinction) at Leibniz Universität Hannover in 2011. He has published more than 50 peer-​reviewed papers and was granted multiple awards, among others an ETH Postdoctoral fellowship and the science award of the German Geodetic Commission. Jan 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 founder and chair of the ISPRS II/WG 6 “Large-​scale machine learning for geospatial data analysis” and (together with colleagues) organizer and chair of the CVPR EarthVision workshops.

 

 

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