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By NASA
4 min read
Expanded AI Model with Global Data Enhances Earth Science Applications
On June 22, 2013, the Operational Land Imager (OLI) on Landsat 8 captured this false-color image of the East Peak fire burning in southern Colorado near Trinidad. Burned areas appear dark red, while actively burning areas look orange. Dark green areas are forests; light green areas are grasslands. Data from Landsat 8 were used to train the Prithvi artificial intelligence model, which can help detect burn scars. NASA Earth Observatory NASA, IBM, and Forschungszentrum Jülich have released an expanded version of the open-source Prithvi Geospatial artificial intelligence (AI) foundation model to support a broader range of geographical applications. Now, with the inclusion of global data, the foundation model can support tracking changes in land use, monitoring disasters, and predicting crop yields worldwide.
The Prithvi Geospatial foundation model, first released in August 2023 by NASA and IBM, is pre-trained on NASA’s Harmonized Landsat and Sentinel-2 (HLS) dataset and learns by filling in masked information. The model is available on Hugging Face, a data science platform where machine learning developers openly build, train, deploy, and share models. Because NASA releases data, products, and research in the open, businesses and commercial entities can take these models and transform them into marketable products and services that generate economic value.
“We’re excited about the downstream applications that are made possible with the addition of global HLS data to the Prithvi Geospatial foundation model. We’ve embedded NASA’s scientific expertise directly into these foundation models, enabling them to quickly translate petabytes of data into actionable insights,” said Kevin Murphy, NASA chief science data officer. “It’s like having a powerful assistant that leverages NASA’s knowledge to help make faster, more informed decisions, leading to economic and societal benefits.”
AI foundation models are pre-trained on large datasets with self-supervised learning techniques, providing flexible base models that can be fine-tuned for domain-specific downstream tasks.
Crop classification prediction generated by NASA and IBM’s open-source Prithvi Geospatial artificial intelligence model. Focusing on diverse land use and ecosystems, researchers selected HLS satellite images that represented various landscapes while avoiding lower-quality data caused by clouds or gaps. Urban areas were emphasized to ensure better coverage, and strict quality controls were applied to create a large, well-balanced dataset. The final dataset is significantly larger than previous versions, offering improved global representation and reliability for environmental analysis. These methods created a robust and representative dataset, ideal for reliable model training and analysis.
The Prithvi Geospatial foundation model has already proven valuable in several applications, including post-disaster flood mapping and detecting burn scars caused by fires.
One application, the Multi-Temporal Cloud Gap Imputation, leverages the foundation model to reconstruct the gaps in satellite imagery caused by cloud cover, enabling a clearer view of Earth’s surface over time. This approach supports a variety of applications, including environmental monitoring and agricultural planning.
Another application, Multi-Temporal Crop Segmentation, uses satellite imagery to classify and map different crop types and land cover across the United States. By analyzing time-sequenced data and layering U.S. Department of Agriculture’s Crop Data, Prithvi Geospatial can accurately identify crop patterns, which in turn could improve agricultural monitoring and resource management on a large scale.
The flood mapping dataset can classify flood water and permanent water across diverse biomes and ecosystems, supporting flood management by training models to detect surface water.
Wildfire scar mapping combines satellite imagery with wildfire data to capture detailed views of wildfire scars shortly after fires occurred. This approach provides valuable data for training models to map fire-affected areas, aiding in wildfire management and recovery efforts.
Burn scar mapping generated by NASA and IBM’s open-source Prithvi Geospatial artificial intelligence model. This model has also been tested with additional downstream applications including estimation of gross primary productivity, above ground biomass estimation, landslide detection, and burn intensity estimations.
“The updates to this Prithvi Geospatial model have been driven by valuable feedback from users of the initial version,” said Rahul Ramachandran, AI foundation model for science lead and senior data science strategist at NASA’s Marshall Space Flight Center in Huntsville, Alabama. “This enhanced model has also undergone rigorous testing across a broader range of downstream use cases, ensuring improved versatility and performance, resulting in a version of the model that will empower diverse environmental monitoring applications, delivering significant societal benefits.”
The Prithvi Geospatial Foundation Model was developed as part of an initiative of NASA’s Office of the Chief Science Data Officer to unlock the value of NASA’s vast collection of science data using AI. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT), based at Marshall, IBM Research, and the Jülich Supercomputing Centre, Forschungszentrum, Jülich, designed the foundation model on the supercomputer Jülich Wizard for European Leadership Science (JUWELS), operated by Jülich Supercomputing Centre. This collaboration was facilitated by IEEE Geoscience and Remote Sensing Society.
For more information about NASA’s strategy of developing foundation models for science, visit https://science.nasa.gov/artificial-intelligence-science.
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Last Updated Dec 04, 2024 Related Terms
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NASA/Ben Smegelsky Employees at NASA’s Kennedy Space Center in Florida and NASA astronaut Victor Glover (right) happily snap a photo of themselves during a visit on Nov. 8, 2024. The employees are part of the agency’s Exploration Ground Systems (EGS), which develops and operates the systems and facilities needed to process and launch rockets and spacecraft for NASA’s Artemis missions. EGS plays a primary role in assembly, launch, and recovery of rockets and spacecraft.
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By NASA
4 min read
NASA AI, Open Science Advance Natural Disaster Research and Recovery
Hurricane Ida is pictured as a category 2 storm from the International Space Station as it orbited 264 miles above the Gulf of Mexico. In the foreground is the Canadarm2 robotic arm with Dextre, the fine-tuned robotic hand, attached. NASA By Lauren Perkins
When you think of NASA, disasters such as hurricanes may not be the first thing to come to mind, but several NASA programs are building tools and advancing science to help communities make more informed decisions for disaster planning.
Empowered by NASA’s commitment to open science, the NASA Disasters Program supports disaster risk reduction, response, and recovery. A core element of the Disasters Program is providing trusted, timely, and actionable data to aid organizations actively responding to disasters.
Hurricane Ida made landfall in Louisiana Aug. 21, 2021, as a category 4 hurricane, one of the deadliest and most destructive hurricanes in the continental United States on record. The effects of the storm were widespread, causing devastating damage and affecting the lives of millions of people.
During Hurricane Ida, while first responders and other organizations addressed the storm’s impacts from the ground, the NASA Disasters program was able to provide a multitude of remotely sensed products. Some of the products and models included information on changes in soil moisture, changes in vegetation, precipitation accumulations, flood detection, and nighttime lights to help identify areas of power outages.
Image Before/After The NASA team shared the data with its partners on the NASA Disasters Mapping Portal and began participating in cross-agency coordination calls to determine how to further aid response efforts. To further connect and collaborate using open science efforts, NASA Disasters overlaid publicly uploaded photos on their Damage Proxy Maps to provide situational awareness of on-the-ground conditions before, during, and after the storm.
Immediate post-storm response is critical to saving lives; just as making informed, long- term response decisions are critical to providing equitable recovery solutions for all. One example of how this data can be used is blue tarp detection in the aftermath of Hurricane Ida.
Using artificial intelligence (AI) with NASA satellite images, the Interagency Implementation and Advanced Concepts Team (IMPACT), based at NASA’s Marshall Space Flight Center in Huntsville, Alabama, conducted a study to detect the number of blue tarps on rooftops in the aftermath of hurricanes, such as Ida, as a way of characterizing the severity of damage in local communities.
An aerial photograph shows damaged roofs from Hurricane Maria in 2017 in Barrio Obrero, Puerto Rico. In the wake of the hurricane, the Federal Emergency Management Agency (FEMA) and United States Army Corps of Engineers distributed 126,000 blue tarps and nearly 60,000 temporary blue roofs to people awaiting repairs on damaged homes. NASA While disasters cannot be avoided altogether, timely and accessible information helps communities worldwide reduce risk, improve response, hasten recovery, and build disaster resilience.
Through an initiative led by NASA’s Office of the Chief Science Data Officer, NASA and IBM are developing five open-source artificial intelligence foundation models trained on NASA’s expansive satellite repositories. This effort will help make NASA’s vast, ever-growing amounts of data more accessible and usable. Leveraging NASA’s AI expertise allows users to make faster, more informed decisions. User applications of the Prithvi Earth Foundation Models could range from identifying flood risks and predicting crop yields to forecasting long range atmospheric weather patterns.
“NASA is dedicated to ensuring that our scientific data are accessible and beneficial to all. Our AI foundation models are scientifically validated and adaptable to new data, designed to maximize efficiency and lower technical barriers. This ensures that even in the face of challenging disasters, response teams can be swift and effective,” said Kevin Murphy, NASA’s chief science data officer. “Through these efforts, we’re not only advancing scientific frontiers, but also delivering tangible societal benefits, providing data that can safeguard lives and improve resilience against future threats.”
Hear directly from some of the data scientists building these AI models, the NASA disaster response team, as well as hurricane hunters that fly directly into these devastating storms on NASA’s Curious Universe podcast.
Learn more about NASA’s AI for Science models at https://science.nasa.gov/artificial-intelligence-science/.
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