Stien Heremans
Stien Heremans obtained her master in Earth Observation from Leuven University in 2009. She then proceeded with a PhD about the use of machine learning methods for sub-pixel crop classification, which she successfully defended in 2015. Since then, she has been working as a remote sensing expert in the Research Institute for Nature and Forest (INBO) of the Flemish government and as a post-doctoral researcher at Leuven University. Her research focuses on the integration of remote sensing data into policy-relevant environmental monitoring and modeling.
Sessions
Evidence-based policy is gaining importance, also in the environmental policy domain in Flanders, Belgium. However, the most prevalent source of policy-relevant information still remains ground sampling, with limited spatial and temporal detail and coverage. The ease of access to freely available (Sentinel) satellite imagery from the Copernicus program through the new OpenEO API provides a golden opportunity for filling this information gap. During the GEO.INFORMED project, remote sensing and deep learning researchers engaged in a co-creation trajectory with regional environmental policy makers to develop machine learning workflows for transforming Copernicus satellite data into policy-relevant geodata. The main challenges encountered in the project where associated with ensuring mutual understanding between scientists and policy-makers; and with the technical implications of non-standard model inputs and limited reference data availability. Within the project, a range of strategies for overcoming these challenges were tested, and the lessons learned will be the main focus of this talk.