About my work and me

Data Science — Satellite Earth Observation — Geoanalysis

Welcome!

My name is Dr. Marius Philipp and I'm currently working as a Lead Data Analyst at Airbus Helicopters in Donauwörth. My focus lies on coordinating and organising the activities of a team of business analysts, data analysts and project managers as well as enhancing and improving the data assessment processes at Airbus Helicopters Engineering. During my PhD, which was a joint research collaboration between the Department of Remote Sensing at the University of Würzburg and the German Aerospace Center (DLR), I was studying the potential of SAR satellite earth observation in combination with deep learning and change vector analyses for monitoring permafrost degradation in Arctic coastal environments on a pan-Arctic scale. Through my studies and working experiences I gained expertise in statistical modelling and machine learning applications, including deep learning, and dealt with diverse data sets. In my free-time I'm passionate about photography, playing the guitar, and enjoy going on hiking trips.

Skills and Experiences

Professionally, I'm interested in statistical analysis and modelling, supervised and unsupervised machine learning approaches for computer vision problems, and software development. In my work as a spatial data scientist I'm particularly focused on combining deep learning with satellite data for analyzing and quantifying environmental changes on large spatial scales.

To conduct my research I utilize a variety of tools and programs, including the programming languages R and Python, cloud computing platforms such as Google Earth Engine, GIS software including ArcGIS and QGIS, and collaborative environments such as GitHub and Overleaf.

In order to tackle a variety of environmental research questions, I acquired knowledge in handling different data types from various sources. I hereby utilize multispectral and hyperspectral optical imagery for monitoring the current state of different vegetation covers. Synthetic Aperture RADAR data is applied for land cover change detection and for quantifying surface deformation via interferometry. Furthermore, biomass estimation and structural analysis can be accomplished through dense point-clouds derived from LiDAR sensors.

3D Terrestrial LiDAR point-cloud of a tropical savanna landscape in Australia.

Pseuo-RGB image of Darwin derived from optical Landsat-8 satellite imagery.

Synthetic Aperture RADAR backscatter image in VV polarisation of the Lena Delta derived from Sentinel-1 satellite imagery.

Career

since May 2023: Lead Data Analyst at Airbus Helicopters, Donauwörth

2019 - 2023: Research Fellow at Julius-Maximilians-Universität, Würzburg

2019: Intern at Remote Sensing Solutions GmbH, Munich

2018: Intern at Commonwealth Scientific and Industrial Research Organisation (CSIRO), Darwin

2017: Intern at German Aerospace Center (DLR), Oberpfaffenhofen

Education

2020 - 2023: Doctoral Student in Remote Sensing Data Science at the Julius-Maximilians-Universität Würzburg in collaboration with the German Aerospace Center (DLR)

2017 - 2019: Master of Science in Applied Earth Observation and Geoanalysis (EAGLE) at the Julius-Maximilians-Universität Würzburg

2014 - 2017: Bachelor of Science in Physical Geography (main subject) and Biology (side subject) at the Julius-Maximilians-Universität Würzburg

Further Training and Certificates

Git & Software

Foundations of Research Software Publication
Version Control using Git

Professional teaching seminar series

Basics of university didactics
Planning courses
Holding lectures for scientists and physicians

Structured doctorate seminar series

Administrative competences
Data protection
Project funding

Scholarships and Awards

Award (2021): MDPI Remote Sensing 2020 Best Cover Award

Scholarship (2018): Scholarship from the German Academic Exchange Service (DAAD) for an internship abroad at the Commonwealth Scientific and Industrial Research Organization (CSIRO) in the Geospatial Ecology and Remote Sensing (GEARS) Lab in Darwin, Australia

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