most citedFoundation Models for Generalist Geospatial Artificial Intelligence

16 citations · 33 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20249 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.LG20241 cited

Evaluating the transferability potential of deep learning models for climate downscaling

Ayush Prasad, Paula Harder, Qidong Yang +4

Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at region…

cs.AI20241 cited

Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation

Michal Muszynski, Levente Klein, Ademir Ferreira da Silva +13

Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration initiatives. Moreover, ve…

cs.CV202316 cited

Foundation Models for Generalist Geospatial Artificial Intelligence

Johannes Jakubik, Sujit Roy, C. E. Phillips +30

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote se…

cs.LG20236 cited

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang, Alex Hernandez-Garcia, Paula Harder +5

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate…