📢Evaluation of ten #satellite-based and #reanalysis #precipitation datasets on a daily basis for #Czechia (2001–2021) by Daniel Paluba et al. @palubad.bsky.social
👉https://doi.org/10.1080/20964471.2025.2592444
💌 #ERA5 #timeseries #meteorology #remotesensing #earthobservation #GIS #opendata
Some takeaways:
• Traditional ML models (linear SVM, ridge and logistic regression and kNN) performed as well as, or better than, more complex models (Random Forest, XGBoost, neural networks - MLPs).
• It suggests that the AEF embedding space already provides a well-structured representation.
• GEM-Forest offers a high potential for straightforward reproducibility for other years of AEF embeddings (2017-2025)
Explore the GEM-Forest in our GEE app: danielp-cuni.projects.earthengine.app/view/gem-forest