IBM and NASA use AI to research climate change

IBM and NASA use AI to research climate change

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IBM and NASA’s Marshall Space Flight Center are to use IBM’s AI technology to glean new insights in NASA’s Earth and geospatial science data.

The joint project will see the application of AI foundation model technology to NASA’s Earth-observing satellite data.

The goal of this work is to provide an easier way for researchers to analyse and draw insights from large datasets gathered through Earth observations.

IBM’s foundation model technology can accelerate the discovery and analysis of this data in order to advance the scientific understanding of Earth and response to climate-related issues.

“The beauty of foundation models is they can potentially be used for many downstream applications,” said Rahul Ramachandran, senior research scientist at NASA’s Marshall Space Flight Centre.

“Building these foundation models cannot be tackled by small teams,” he added. “You need teams across different organisations to bring their different perspectives, resources, and skill sets.”

Both parties plan to develop new technologies to extract insights from Earth observations. One project will train an IBM geospatial intelligence foundation model on NASA’s Harmonized Landsat Sentinel-2 (HLS) dataset, a record of land cover and land use changes captured by Earth-orbiting satellites.

In doing so, they hope to identify changes in the geographic footprint such as natural disasters, cyclical crop yields, and wildlife habitats.

The two also hope to create an easily searchable body of Earth science literature, for which IBM has developed an NLP model trained on nearly 300,000 Earth science journal articles and trained on Red Hat’s OpenShift, to organise the literature and make it easier to discover new information.

“Foundation models have proven successful in natural language processing, and it’s time to expand that to new domains and modalities important for business and society,” said Raghu Ganti, principal researcher at IBM.

“Applying foundation models to geospatial, event-sequence, time-series, and other non-language factors within Earth science data could make enormously valuable insights and information suddenly available to a much wider group of researchers, businesses, and citizens. Ultimately, it could facilitate a larger number of people working on some of our most pressing climate issues.”

Future joint projects include constructing a foundation model for weather and climate prediction using MERRA-2, a dataset of atmospheric observations.

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