Author

Srinivasan Keshav

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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

#machine learning Preprint Sep 2026

Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features

Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping that is accurate for individual forest parcels yet deployable continent-wide. Geospatial foundation model (GFM) embeddings enable label-sca...

Thomas Ratsakatika, Mihai Zotta, Srinivasan Keshav et al. · 0 citations