NASA has recognized a team of researchers from IBM, NASA, and academic partners for their work on open-source AI foundation models designed to help scientists analyze massive amounts of Earth and space data more efficiently.
The collaboration centers on a family of models known as Prithvi, which are trained on large volumes of satellite and geospatial data. Instead of being built for a single task, these models can be adapted to many scientific problems, such as identifying climate patterns, mapping flood damage, or analyzing planetary surfaces.
The team recently received a NASA Group Achievement Award, recognizing the impact of this work across Earth science and space research. Engineers and researchers from IBM, NASA, and several universities contributed to the project.
A major advantage of these models is reuse. Scientists can start with a pretrained foundation model rather than building a new AI system from scratch for every dataset. This approach saves time and computing resources while making advanced AI tools more accessible to researchers.
The Prithvi models are also released as open source, allowing scientists around the world to use, adapt, and improve them. Supporting tools make it easier to fine-tune the models for specific research needs, helping accelerate scientific discovery in areas like climate monitoring, weather analysis, and environmental research.
Foundation models are increasingly being used behind the scenes to tackle complex global challenges. While much of today’s AI attention is focused on consumer chatbots, this project highlights a different direction of AI as a practical tool for large-scale scientific work.
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