AI-Observer Summer School

The first AI-Observer Summer School was held at the Eratosthenes Center of Excellence in Limassol, Cyprus, from July 10-14. Training sessions were given by Prof. Fabio Del Frate, Giorgia Guerrisi and Lorenzo Giuliano Papale (Tor Vergata University of Rome), and Dr. Gerd Reis (German Research Center for Artificial Intelligence). During the five-day hybrid event, more than 50 participants learned about the application of artificial intelligence in Earth observation, with special focus on disaster risk management. Topics included deforestation, flood detection, and natural hazard management using Sentinel-1 Synthetic Aperture RADAR (SAR), and Sentinel-2 Multi-Spectral Imaging (MSI) data.

AI-OBSERVER consortium:
Eratosthenes Center of Excellence
Deutsches Forschungszentrum für Künstliche Intelligenz
Università degli Studi di Roma Tor Vergata
Cellock Ltd.

Michael Lorenz won a best Paper award

We are glad to announce that our colleague Michael Lorenz won a best Paper award for his work On Motions artifacts arising when integrating inertial sensors into loose clothing such as a working jacket.


  • Inertial human motion capture (IHMC) has become a robust tool to estimate human kinematics in the wild such as industrial facilities.
  • In contrast to optical motion capture, where occlusions might take place, the kinematics of a worker can be continuously provided.
  • This is for instance a prerequisite for an ergonomic assessments of the workers.
  • State-of-the-art IHMC solutions require inertial sensors to be tightly attached to body segments.
  • This requires an additional setup time and lowers the practicability and ease of use when it comes to an industrial application.
  • In contrast, sensors integrated into loose clothing such as a working jacket, may yield corrupted kinematics estimates due to the additional motion of loose clothing.
  • In this work we present a study of orientations deviations obtained from kinematics estimates using tightly attached inertial sensors and into a working jacket integrated ones.
  • We performed a quantitative analysis using data from the two hardware setups worn by 19 subjects performing different industry related tasks and measures of their body shapes.
  • Using this data we approximated probability distributions of the deviation angles for each person and body segment.
  • Applying different statistical measures we could gain insights to questions like, how severe orientation deviations are, if there is an influence of body shapes on the distribution and how probability distributions of the deviation angles can indicate physical motion limitations of a sensor attached to a segment.