1 citations · 3 across the 5 of their papers we have counts for
5 papers
Foundation Models Meet Agriculture: Challenges Beyond Pretraining
Vishal Nedungadi, Xingguo Xiong, Marc Rußwurm +1
Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models have emerged as powerful, lab…
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…
AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
Vishal Nedungadi, Muhammad Akhtar Munir, Marc Rußwurm +5
Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization, contributing significantly to morbidity and mortality rates. In this pa…
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…