Geospatial machine learning (ML) models are widely used in scientific and (semi)operational settings by geoscientists, ecologists, agronomists, engineers, spatial planners, public health specialists, etc. While some researchers and practitioners are proficient in the development, application and (re)use of ML models, others are lacking the basic knowledge required to harvest the benefits of geospatial ML models. Additionally, ML modelling remains an art and modellers do not always document their creative process. To address these problems, we propose creating a geospatial ML course that increases geospatial ML literacy as well as the (re)usability of geospatial ML models. The geospatial ML course would not only provide researchers with foundational knowledge and skills, but also with the opportunity to stay updated with the latest advancements.
The work packages and leading partners are:
- WP0: Coordination and Outreach (ITC)
- WP1: Core Concepts and Alignment (NLeSc)
- WP2: Geospatial ML modelling (WUR and ITC)
- WP3: Model reusability and reproducibility (ITC)
