2023-10-04, 15:45–16:30, EURAC Seminar room 2 & 3
Reproducibility and Reusability of workflows are increasingly important topics in Remote Sensing research when moving towards FAIR and open data science. This workshop discusses the current status quo, and how we can improve this with future activities.
The field of Remote Sensing in Earth Sciences has grown rapidly in recent years, providing valuable data and insights into a wide range of environmental phenomena. This development is driven by better access to data and computational resources provided by cloud processing platforms. The upcoming Digital Twins, though conceptually simple, come at a technologically large cost.
As research results are increasingly based on big data and complex methods, the ability to reproduce and validate them has become a significant challenge. Making large datasets available along with code and text has been identified as one of the key bottlenecks. Other roadblocks for reproducibility in Remote Sensing include restrictive data licenses, heterogeneity of software and computing environments, and code literacy.
Nevertheless, reproducibility entails benefits such as advancing method development, improving the learning process, and building trust in the result through transparency. It therefore has positive impact on science itself, education, and decision-making.
This interactive workshop aims to bring together Remote Sensing experts to discuss the current status of and future strategies for reproducibility in the field. The first part provides short contributions about recent activities that facilitate reproducible Remote Sensing today. In the second part participants will try to formulate a vision or roadmap on how reproducibility should look when working towards FAIR and open data science.
OEMC Grant agreement ID: 101059548
Edzer Pebesma leads the spatio-temporal modelling laboratory at the institute for geoinformatics, and is currently head of institute. He holds a PhD in geosciences, and is interested in spatial statistics, environmental modelling, geoinformatics and GI Science, optimizing environmental monitoring, but also in e-Science and reproducible research. He is an ordinary member of the R foundation and is one of the authors of the freely online book "Spatial Data Science: With applications in R".