Author

U. Cambridge

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#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

#machine learning Preprint Sep 2026

Orukeet: Multilingual ASR with Frozen Gabor Kernels

Orukeet replaces half of an adapted Parakeet encoder's temporal filters with 12,288 fitted Gabor kernels, freezes these replacements, and trains the remaining parameters on multilingual and multi-accent data. Final adaptation and checkpoint selection use LibriSpeech test-other. Across 20,146 FLEURS recordings in 25 lan...

N. Roll, Irene Yi, Büsra Marsan et al. · 0 citations

#machine learning Preprint Aug 2026

Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers address this gap using...

Pedro Sousa, Will Tebbutt, S. Jaffer et al. · 0 citations