A vital view of our Planet
It could be just the awe-inspiring images captured by Earth-observing satellites, which demonstrate undoubtedly the beauty of the Earth. But, there is so much more than that: what makes the Satellite Technology so valuable is its crucial role on gathering information about the complexity of the Earth system and offering a holistic view of the environment, resources, built –up areas. Armed with this data, the distinguished Professor Dr. Xiaoxiang Zhu, Head of Department “EO Data Science”, at the German Aerospace Center, aims to develop explorative algorithms, in order to answer important geoscientific questions, such as understanding the patterns of global urbanization. In her interview below, she refers –among other things- to her current research activities while she underlines the significance of Earth Observation for our future.
Ms. Professor Zhu, you work on signal processing for Earth Observation. What motivated you to focus on environmental data science?
Space has fascinated me since I was a child. I was also interested in mathematically demanding tasks from a very early age; as a schoolgirl I took part in mathematics competitions and always enjoyed dealing with challenging tasks. On the other hand, I would like to make a difference with my research, preferably in an interdisciplinary team. So what I am researching on today is no coincidence: The use of satellite technology, as it occurs in earth observation, perfectly combines my passion for space with my mathematical skills and interests. In addition, the working conditions at the German Aerospace Center (DLR) and the Technical University of Munich (TUM) are fantastic: As an Earth observation scientist, at DLR I am involved in large satellite projects and have access to up-to-date data from the most modern satellites, on the other hand, at TUM, I can train students, inspire them for my research topics and win them as doctoral candidates.
Can you kindly walk us to your current research activities?
I have been awarded an ERC Starting Grant, which will keep me scientifically busy for the next few years. In this project, methods and algorithms of machine learning are to be developed to fuse Peta bytes of remote sensing data from different satellite missions with massive image data and text files from social networks. This enables cities worldwide and their developments in 3D and 4D to be mapped, buildings/infrastructure types to be classified, population density to be estimated transparently, both in high resolution and globally. The result should be a new kind of global city model, which is, for example, particularly important for developing countries with their rapidly growing cities and slums.
On the other hand, Earth observation has irreversibly arrived in the Big Data era with the Sentinel satellites (and in the future with Tandem-L). This requires not only new technological approaches to manage large amounts of data, but also new analysis methods. Since April 2018, I have been promoted as the head of the department EO data science at the Earth Observation Center of DLR. Our mission is to develop explorative algorithms, in particular methods from artificial intelligence and data science, for the big Earth data analytics in general, aiming at answering important geoscientific research questions, for which understanding global urbanization is one example.
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