Abstract
Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.
Guido Kraemer
Postdoctoral associate / Earth System Data Science
My research interests include the interactions between society and biosphere. I have been working on the extraction of the global dynamics of ecosystems and society. I have an interest in using machine learning and multivariate statistics to understand the behavior of complex systems.
Ida Flik
Research Fellow / Earth System Data Science
I work across the fields of data, journalism and design in different capacities. At RSC4Earth, I work on use cases for the “Social Data Cube” (an NDFI4Earth pilot ) and vulnerability to extreme events / tropical cyclones.
Sarah Habershon
Research Assistant / Earth System Data Science
I am a research assistant on the CoCap-CoV-2 project; Coping Capacity of nations facing systemic crisis - a global intercomparison exploring the SARS-CoV-2 pandemic. A former data journalist and policy analyst, I specialise in investiative analysis and data-driven storytelling.
Chaonan Ji
Postdoctoral fellow / Earth System Data Science
My research interests include the classification of hyperspectral images from air and space, gradient analysis, and its application in urban areas. My current research interest is in the study and analysis of environmental responses to climate extremes.
Martin Reinhardt
Postdoctoral fellow / Earth System Data Science
My research interests is the application of machine learning algorithms on Earth System Data Cubes to extract new information of global dynamics of ecosystems and extreme events. In this setting I have a strong focus on use deep learning methods and neural networks but also more classic approaches.
Maximilian Söchting
PhD candidate / Earth System Data Science
My research interests include computer graphics, interactive applications and data compression algorithms. I am working on an interactive visualization of remote sensing data in a collaboration between the Image and Signal Processing Group and the Earth System Data Science group.
