Dear colleagues and friends,

Parallel to my professorship at the Technical University of Munich (TUM) – Professor for signal processing in Earth Observation (TUM-SiPEO), which is a joint professorship with the German Aerospace Center (DLR), since April 1st, 2018, I am heading the fifth department at the remote sensing technology institute (IMF):

Department of EO Data Science

with the abbreviation "MF-DAS". Please see here for an overview of the current organization of IMF.

The new department is made up of my previous group in MF-SAR and the group of Prof. Dr. Mihai Datcu from MF-PBA and will deal with methods of artificial intelligence, machine learning, data analytics, data mining for earth observation.

Background:

The previous departments at IMF are expertise pools in the three remote sensing technologies SAR, optics and atmospheric spectrometry. This continues to be one of the great strengths of DLR-IMF. For years, however, cross-technology processes, e.g. for data fusion, have been gaining in importance. Furthermore, Earth observation with the Sentinel satellites (and in the future with Tandem-L) has irreversibly arrived in the Big Data era. This requires not only new technological approaches to managing large amounts of data as pursued by own twin institute German Remote Sensing Data Center (DFD), but also new evaluation methods. Here, methods of data science and artificial intelligence, such as machine learning, become indispensable. Deep Learning in particular has led to a revolution in AI in recent years and must be developed for earth observation much more than before. IMF also recognized this trend at an early stage and have been working on this topic for a long time; more than ten doctoral students are currently working on it. The Helmholtz evaluation that has just taken place has once again confirmed that we are on the right track. Motivated by all of these, we founded this new department to present all these topics.

Our missions are:

We develop explorative model-based signal processing algorithms to improve information retrieval from remote sensing data of current and the next generation EO mission

-        We stay “in the center of explosion” for Artificial Intelligence for Earth Observation (AI4EO)

          -         We develop sophisticated algorithms and discover novel applications crossing sensor technologies

-         We explore Big Earth Data Analytics from knowledge discovery, HPC to geoscientific applications

-         We harvest unconventional Geo data sources, such as social media and NewSpace

I am very excited about all these new developments and welcome all suggestions, and collaborations!

Yours,

Xiaoxiang Zhu

Signal Processing in Earth Observation
Prof. Xiaoxiang Zhu

Technische Universität München
Arcisstr. 21
D-80333 München

 

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ESA Living Planet 2019 abstract deadline
11/11/2018