18 citations · 40 across the 11 of their papers we have counts for
4 papers · 1 filter
Transferability and operational reliability of a Prithvi crop classification foundation model under phenological and geographic shift across three continents
Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane +7
Fine-tuned geospatial foundation models (GeoFMs) pretrained on large satellite archives have been shown to improve crop classification accuracy and geographic transferability. Howe…
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
Rajat Shinde, Christopher E. Phillips, Kumar Ankur +10
High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific appli…
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…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…