NYSE-listed IBM and the US National Aeronautics and Space Administration’s (Nasa’s) Marshall Space Flight Centre have teamed up to leverage IBM’s artificial intelligence (AI) technology to discover new insights in Nasa’s massive trove of Earth and geospatial science data.

The collaboration will apply AI foundation model technology to Nasa’s Earth-observing satellite data for the first time.

According to a joint statement, foundation models are types of AI models that are trained on a broad set of unlabelled data, can be used for different tasks and can apply information about one situation to another.

These models have rapidly advanced the field of natural language processing (NLP) technology over the last five years, and IBM is now pioneering applications of foundation models beyond language.

“Earth observations that allow scientists to study and monitor our planet are being gathered at unprecedented rates and volume. New and innovative approaches are required to extract knowledge from these vast data resources,” the partners point out, adding that the aim of the collaboration is to provide an easier way for researchers to analyse and draw insights from these large datasets.

“IBM’s foundation model technology has the potential to speed up the discovery and analysis of these data in order to quickly advance the scientific understanding of Earth and response to climate-related issues.”

IBM and Nasa plan to develop several new technologies to extract insights from Earth observations, with one project training an IBM geospatial intelligence foundation model on Nasa’s Harmonized Landsat Sentinel-2 dataset, which is a record of land cover and land use changes captured by Earth-orbiting satellites.

“By analysing petabytes of satellite data to identify changes in the geographic footprint of phenomena such as natural disasters, cyclical crop yields and wildlife habitats, this foundation model technology will help researchers provide critical analysis of our planet’s environmental systems,” the statement outlines.

Another output from this collaboration is expected to be an easily searchable corpus of Earth science literature.

IBM has developed an NLP model trained on nearly 300 000 Earth science journal articles to organise the literature and make it easier to discover new knowledge.

The fully trained model, containing one of the largest AI workloads trained on Red Hat’s OpenShift software to date, uses IBM’s open-source multilingual question-answering system PrimeQA.

Beyond providing a resource to researchers, the new language model for Earth science could be infused into Nasa’s scientific data management and stewardship processes.

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

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

IBM principal researcher Raghu Ganti adds that foundation models have proven successful in natural language processing.

“It is time to expand that to new domains and modalities important for business and society. 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, he says, it could facilitate a larger number of people working on some of the most pressing climate issues.

The agreement between the parties include potential IBM-Nasa joint projects such as constructing a foundation model for weather and climate prediction using MERRA-2, a dataset of atmospheric observations.

The collaboration is part of Nasa’s Open-Source Science Initiative, a commitment to building an inclusive, transparent and collaborative open science community over the next decade.