Modern research in the field of Earth Observation (EO) and sustainability presents challenges for computing, storage, and communication infrastructure required to analyze the vast amounts of data from remote sensing systems. To develop, execute, optimize, and maintain EO workflows, geoscientists need in-depth expertise in IT and data sciences.
Developers face challenges such as:
- A large number of algorithms
- Contradictory recommendations
- Non-transparent data (pre)processing
- Lack of standards for workflow specification
This leads to limited reusability of workflow implementations and poor reproducibility of results. Furthermore, processing vast amounts of data (petabytes) from distributed and heterogeneous sources presents a significant entry barrier for geoscientists.
The main goal of this research group is to develop a framework consisting of a set of methods, models, and architectures that serve as the foundation for serverless scientific computing and engineering. In serverless computing, computing, storage, and communication resources are abstracted.
- Automation, sharing, and reuse of EO workflows across projects, teams, application domains, and organizations.
- Elimination of technical entry barriers for developing and executing complex, large-scale EO workflows involving multiple distributed and heterogeneous data sources.
We envision a component-based workflow engineering methodology that supports the composition of complex processing workflows from ready-to-use, generic or domain-specific components—without requiring expert knowledge of the algorithms used or their internal implementation details.
We believe these goals can only be achieved through collaboration between computer scientists, geoscientists, and ecologists in an interdisciplinary research environment. To this end, part of the research group is led by geoscientists and ecologists and focuses on the following domain-specific research objectives:
- (a) Investigating the impact of land surface dynamics on the movement patterns of migrating animals as a basis for predicting future space usage and species composition.
- (b) Examining the effects of climate change on snow cover and melting.
