Venue: Date and time: 26 August 2024, 14.00-15.30 h OnlineRegister here Project working title: Increasing literacy, use and reuse of geospatial machine learning modelsThis project idea was approved for further development by the TDCC-NES Governing Board within the 2nd submission cycle of the current call for proposals. The Project initiative meeting is a chance for the broader NES community to learn more about it and offer their input.  Project idea description: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 proposed course will provide valuable insights into the development and application of ML concepts, while addressing the unique requirements of geospatial data.  Three hallmarks of the proposed course can be highlighted:  It will be developed using open-source tools and solutions. This will help to scale up the work, allowing (sub)disciplines to reuse, expand and modify developed materials.  Ways to ensure model FAIRness and reproducibility by adopting and adapting open-source (MLOps) tools and solutions will be explored and tested.  The research community will be involved from the beginning of lesson and educational material development, to adapt the material to their use cases, and to continually gather their feedback. Lead applicants: Raul Zurita-Milla, University of Twente Claire Donnelly, NLeSC Listed project partners:WUR – Ioannis Athanasiadis VU Amsterdam – Dim Coumou TU Delft – Stef Lhermitte LTER-LIFE – Geerten Hengeveld SENSE / WIMEK – Peter Vermeulen OSCNL – Anna van ‘t Veer NLRN – Michiel de Boer Are you working with geospatial data & ML models? Are you supporting researchers on these topics? Register to join the meeting, learn more about the project idea, contribute with your insight, and offer input for developing a full project proposal.