Christian Geiss

risk assessment, natural hazard research

About

Christian is with the team “Modeling and geostatistical analysis” at the German Aerospace Center (DLR). He worked on several topics related to renewable energy, earthquake risk assessment, and methods for remote sensing data processing. His current research interests include techniques of multimodal remote sensing, machine learning, and pattern recognition, specifically applied to the field of natural hazard risk research.

 

Dr. Christian Geiss, DLR-EOC

Recent News

EAGLE M.Sc. idea presentations

EAGLE M.Sc. idea presentations

On Monday, 24th of September from 1:30 onwards the following EAGLE students will present their M.Sc. idea. Everybody is welcome to join their presentations and to provide feedback: Julia: "Time-Series Analysis of Sentinel-1 and Sentinel-2...

EAGLE summer dialogue 2018

EAGLE summer dialogue 2018

Our 2018 EAGLE summer dialogue took place last Friday, 22nd of June and was a great place to meet all students, lectures, staff of the department and quite some external guests from all around Europe.

EAGLE students learn remote sensing field work

EAGLE students learn remote sensing field work

The course "from field work to spatial data" by Tobias Ullmann and Martin Wegmann is covering the whole range of field campaign planning and especially training all necessary methods such as GPS handling, coordinate systems, setting waypoints or finding locations. In...

EAGLE students coding with sweets

EAGLE students coding with sweets

Today our EAGLE students applied data munging, pipes, plotting and statistics using colour distribution of sweets. They specifically used the dplyr, ggplot, kableExtra and others to compute derivatives, rearrange the data, plot it and run statistics on colour...

Spatial Python block course

Spatial Python block course

Last week Steven Hill and Thorsten Dahms gave a course that introduced EAGLE students to Python-based spatial data analysis. The advantages and challenges of different python libraries, data sets and methods were covered in hands-on exercises and also discussed...