TDCCs Incrreasing literacy

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. These models and the methods to develop them are continuously evolving and changing rapidly, making it difficult to keep up with them. 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 modelers 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. Although generic ML courses exist, using ML with geospatial data is different from other domains, as
the spatial aspect introduces domain specific challenges (e.g., ways to deal with spatial autocorrelation). Moreover, the variability, volume and dimensionality of geospatial data often brings data integration and processing challenges. Next to this, modelers often look for geographical and physical consistency whereas this is not automatically guaranteed by ML algorithms. Additional challenges include the selection of methods to properly evaluate geospatial ML models, the delineation of their domain of applicability so that they (re)used in a responsible manner and the identification of suitable ways to combine geospatial ML and legacy (mechanistic, mathematical, process-based) models. In short, the proposed course will provide valuable insights into the development and application of ML concepts, while addressing the unique requirements of geospatial data. Finally, we highlight three hallmarks of the proposed course: 1/ we will develop it using opensource tools and solutions. This will help to scale up our work, allowing (sub)disciplines to reuse, expand and modify our materials; 2/ we will explore and test ways to ensure model FAIRness and reproducibility by adopting and adapting open-source (MLOps) tools and solutions. This will lead to more transparent and (re)usable models that can better support policy/decision making, and 3/ we will involve the research community from the beginning of lesson and educational material development, to adapt the material to their use cases, and to continually gather their feedback.