We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.
- Pub Date:
- June 2017
- Data Science;
- Machine Learning;
- Astrophysics - Instrumentation and Methods for Astrophysics
- 10 pages, 5 figures, IAU Symposium 325, "Astroinformatics"