Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements.

Advances in geospatial and Machine Learning techniques for large datasets of georeferenced observations have made it possible to produce model-based global maps of ecological and environmental variables. However, the implementation of existing scientific methods (especially Machine Learning models) to produce accurate global maps is often complex. (co-founder of OpenGeoHub foundation), (researcher at ETH Zürich), and (Science IT Consultant at the University of Zürich) shared with their perspectives for creators and users of these maps, focusing on the key challenges in producing global environmental geospatial datasets to achieve significant impacts.