Physiography improvements in numerical weather prediction digital twin engines

The next generation of numerical weather prediction model (so-called digital twin engines) will reach hectometric scale, for which the existing physiography databases are insufficient. Our work leverages machine learning and open-access data to produce a more accurate and higher resolution physiography database. One component to improve is the land cover map. The reference data gathers multiple high-resolution thematic maps thanks to an agreement-based decision tree. The input data are taken from the Sentinel-2 satellite. Then, the land cover map generation is made with image segmentation. This work implements and compares several algorithms of different families to study their suitability to the land cover classification problem. The sensitivity to the data quality will also be studied. Compared to existing work, this work is innovative in the reference map construction (both leveraging existing maps and fit for end-user purpose) and the diversity of algorithms to produce our land cover map comparison.