1 citations · 3 across the 5 of their papers we have counts for
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SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
Maria Gonzalez-Calabuig, Kai-Hendrik Cohrs, Vishal Nedungadi +7
Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for…
From General to Specialized: The Need for Foundational Models in Agriculture
Vishal Nedungadi, Xingguo Xiong, Aike Potze +4
Food security remains a global concern as population grows and climate change intensifies, demanding innovative solutions for sustainable agricultural productivity. Recent advances…
MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
Vishal Nedungadi, Ankit Kariryaa, Stefan Oehmcke +3
The volume of unlabelled Earth observation (EO) data is huge, but many important applications lack labelled training data. However, EO data offers the unique opportunity to pair da…