Using IBM's artificial intelligence (AI) technology, NASA's Marshall Space Flight Center and IBM will work together to glean new insights from the vast repository of Earth and geospatial science data that NASA maintains. For the first time, the collaborative endeavour will use Earth-observing satellite data from NASA and AI foundation model technologies.
Foundation models are classes of AI models that can be used to a variety of tasks, are trained on a large amount of unlabeled data, and can transfer knowledge from one context to another. Over the past five years, these models have quickly expanded the field of natural language processing (NLP) technology, and IBM is at the forefront of using foundation models for applications outside of language.
Earth observation data are being gathered at unprecedented rates and volumes, allowing scientists to study and monitor our world. To extract knowledge from these enormous data resources, novel and creative ways are needed. The objective of this effort is to make it simpler for academics to examine and extrapolate information from these huge datasets. The ability to identify and analyse these data more quickly because to IBM's foundation model technology could help increase science's comprehension of Earth and its reaction to climate-related problems.
In order to get knowledge from Earth observational data, IBM and NASA intend to create a number of innovative technologies. In one project, NASA's Harmonized Landsat Sentinel-2 (HLS) dataset, a record of land cover and land use changes acquired by Earth-orbiting satellites, will be used to train an IBM geospatial intelligence foundation model. This foundation model technology will assist researchers in providing critical analyses of our planet's environmental systems by analysing petabytes of satellite data to discover variations in the geographic footprint of phenomena like natural disasters, cyclical crop yields, and wildlife habitats.
To glean insights from Earth monitoring, IBM and NASA intend to create a number of new technologies. NASA's Harmonized Landsat Sentinel-2 (HLS) dataset, a record of changes in land cover and land use acquired by Earth-orbiting satellites, will be used in one project to train an IBM geospatial intelligence foundation model. This foundation model technology will assist researchers in conducting critical analyses of the environmental systems that govern our planet by analysing petabytes of satellite data to spot changes in the geographic footprint of phenomena like natural disasters, cyclical crop yields, and wildlife habitats.
"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 Center in Huntsville, Alabama. "Building these foundation models cannot be tackled by small teams," he added. "You need teams across different organizations to bring their different perspectives, resources, and skill sets."
"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."
Other potential cooperation efforts between IBM and NASA under this agreement include developing a fundamental model for forecasting weather and climate utilising the MERRA-2 dataset of atmospheric observations. This partnership is a part of NASA's Open-Source Science Initiative, which is dedicated to creating a diverse, open, and cooperative open science community over the course of the next ten years.
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