Author

Robin Young

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#machine learning Apr 2026

Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features

It is shown that self-supervised learning applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness across diverse environments, and SSL-derived features are the most informative tested predictor group, and outperform each of the established climate, soil, and land cover baselines.

Robin Young, M. V. van Nuland, E. T. Kiers et al. · arXiv.org · 0 citations

Jul 2026

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps

This paper presents a concise, end-to-end account of the recommended practices spanning the pipeline from satellite data to an operational map product, organized around six interconnected themes: the EO data infrastructure landscape, data selection and preprocessing, ML dataset construction and model training, uncertai...

Ghjulia Sialelli, Robin Young, Yu-Chang Jiang et al. · arXiv.org · 1 citation