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20232026
most citedFoundation Models for Generalist Geospatial Artificial Intelligence

16 citations · 48 across the 16 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

Now We Know? A Systematic Comparison of TerraMind and THOR

Frederick Schindlegger, Kenzo Bounegta, Eva Gmelich Meijling +5

Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, ho…

cs.LG2026

Partial recovery of meter-scale surface weather

Jonathan Giezendanner, Qidong Yang, Eric Schmitt +9

Near-surface weather varies over tens to hundreds of meters, yet remains unresolved in analyses and forecasts. We test whether this variation can be inferred without resolving atmo…

cs.LG2024★ 2 cited

Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data

Qidong Yang, Jonathan Giezendanner, Daniel Salles Civitarese +8

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, forecasts produced by m…

cs.LG2024★ 9 cited

Prithvi WxC: Foundation Model for Weather and Climate

Johannes Schmude, Sujit Roy, Will Trojak +26

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing numbe…

cs.LG2023★ 15 cited

AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

S. Karthik Mukkavilli, Daniel Salles Civitarese, Johannes Schmude +12

Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been incr…