The collaboration centers on using artificial intelligence models to analyze the impact of climate change.
IBM and NASA’s Marshall Space Flight Center have announced 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 two partners plan to apply AI foundation model technology to NASA’s Earth-observing satellite data for the first time.
Digesting “massive amounts” of raw info
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.
One of the main bottlenecks to applying machine learning to remote sensing data has often been a shortage of training examples. Foundation models ingest massive amounts of raw data and can find their underlying structure without explicit instruction.
Using AI to analyze satellite datasets
In a second project, IBM is building a foundation model to make it easier to develop AI applications to analyze satellite data at scale. IBM is starting with the HLS dataset, and if successful, will tackle MERRA-2, a dataset of atmospheric observations that could improve applications for weather and climate prediction.
Rahul Ramachandran, a senior research scientist at NASA’s Marshall Space Flight Center, highlighted the wide range of applications for the partnership. “It won’t just be NASA that benefits, other agencies and organizations will too,” he said.
“We hope that these models will make information and knowledge more accessible to everyone and encourage people to build applications that make it easier to use our datasets to make discoveries and decisions based on the latest science.”