Spatio-temporal deep learning workflows for transforming remote sensing data into geo-indicators for environmental policy support

The GEO.INFORMED project will develop generic deep learning algorithms adjusted to the EO data characteristics. Calibration and validation is an indispensable part of deep learning. We will develop generic cal/val procedures, with guidelines for ground sampling design and the re-use of existing data. The needs of its end-user community are the driving force behind the development of geo-environmental indicator. In this project, the selection of the geo-indicators and the continuous evaluation of their development will be achieved by a process of co-design. There are to this day only few examples of its use with policy makers as the end-users. The GEO.INFORMED project will thus be a test case for the use of co-design at the science-policy interface.

Keywords:vegetatie, EVINBO, remote-sensing, omgevingsdrukken, waterkwaliteit, monitoring