WueCampus

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

This course builds up on the first-semester course "Introduction into Programming" and features the following three topics:

1.) Development (web API communication; development using version control, foreign code, unit testing & continuous integration)
3.) Machine Learning (algorithmic vs. statistical Modeling; representation learning; Deep Neural Networks & Deep Learning; Machine Learning in Remote Sensing)
4.) Data fusion (time series data fusion; SAR-optical data fusion)

Natural Hazards – Rapid mapping and Analysis by means of Earth Observation data

Always Mondays starting at 12:15

Aim: The objectives of the course are to: 1) Give a general knowledge on how remote sensing data of the different sensors (optical, SAR, thermal) are operationally used for crisis response; 2) Teach the limitations of currently operationally used state-of-the-art rapid damage mapping techniques and how one could overcome these limitations by more advanced techniques (incl. PolInSAR); 3) Teach how remote sensing combined with terrestrial observations can provide an added value to geoscientific research of e.g., volcanological processes

Content: Almost every week a natural disaster happens somewhere around the world. Floods wash away communities under torrents of water, volcanic eruptions destroy houses and infrastructure, forests go up in flames, settlements collapse after an earthquake, roads and bridges are torn away by landslides. Major disasters could threaten the life of thousands. To support search and rescue teams, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area, including grade and type of damage. Thereby, satellite remote sensing plays a key role due to its fast response, wide field of view, and low cost. Satellite sensors enable monitoring of the entire planet, they can “see” through the clouds, detect fires and storms, and even measure the slightest movements in the Earth’s crust. The purpose of this class is to study which type of satellite sensor is suitable for rapid mapping and analysis of the different types of natural disasters, how multi-sensor data approaches can overcome current limitations in operational crisis response, and how the combination of satellite data with terrestrial measurements can provide an added value to geoscientific research.