New IBM Foundation Model Technology Leverages
NASA Earth Science Data for Geospatial Intelligence
HEIGHTS, N.Y., Feb. 1, 2023
/CNW/ -- IBM (NYSE: IBM) and NASA's Marshall Space Flight Center
today announce a collaboration to use IBM's artificial intelligence
(AI) technology to discover new insights in NASA's massive trove of
Earth and geospatial science data. The joint work will apply AI foundation model
technology to NASA's Earth-observing satellite data for the first
Foundation models are types of AI models that are trained on a
broad set of unlabeled 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 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 goal of this work is to provide
an easier way for researchers to analyze 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. 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. By
analyzing 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.
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 organize the literature and make it easier to
discover new knowledge. Containing one of the largest AI workloads
trained on Red Hat's OpenShift software to date, the fully
trained model uses PrimeQA, IBM's open-source multilingual
question-answering system. Beyond providing a resource to
researchers, the new language model for Earth science could be
infused into NASA's scientific data management and stewardship
"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
Other potential IBM-NASA joint projects in this agreement
include constructing a foundation model for weather and climate
prediction using MERRA-2, a dataset of atmospheric observations.
This 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.
For more information about this collaboration, visit the
IBM Research Blog.
Statements regarding IBM's future direction and intent are
subject to change or withdrawal without notice, and represent goals
and objectives only.
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