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By NASA
2 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Here you see the X-59 scaled model inside the JAXA supersonic wind tunnel during critical tests related to sound predictions.JAXA Researchers from NASA and the Japanese Aerospace Exploration Agency (JAXA) recently tested a scale model of the X-59 experimental aircraft in a supersonic wind tunnel located in Chofu, Japan, to assess the noise audible underneath the aircraft.
The test was an important milestone for NASA’s one-of-a-kind X-59, which is designed to fly faster than the speed of sound without causing a loud sonic boom.
When the X-59 flies, sound underneath it – a result of its pressure signature – will be a critical factor for what people hear on the ground.
The X-59 is 99.7 feet long, with a wingspan of 29.7 feet. The JAXA wind tunnel, on the other hand, is just over 3 feet long by 3 feet wide.
So, researchers used a model scaled to just 1.62% of the actual aircraft – about 19 inches nose-to-tail. They exposed it to conditions mimicking the X-plane’s planned supersonic cruising speed of Mach 1.4, or approximately 925 miles per hour.
The series of tests performed at JAXA allowed NASA researchers to gather critical experimental data to compare to their predictions derived through Computational Fluid Dynamics modeling, which include how air will flow around the aircraft.
This marked the third round of wind tunnel tests for the X-59 model, following a previous test at JAXA and at NASA’s Glenn Research Center in Ohio.
The data will help researchers understand the noise level that will be created by the shock waves the X-59 produces at supersonic speeds.
The shock waves from traditional supersonic aircraft typically merge together, producing a loud sonic boom. The X-59’s unique design works to keep shock waves from merging, will result in a quieter sonic thump.
The X-59 was built in Palmdale, California at contractor Lockheed Martin Skunk Works and is undergoing final ground tests en route to its historic first flight this year.
NASA’s Quesst mission aims to help change the future of quiet supersonic travel using the X-59. The experimental aircraft allow the Quesst team to gather public feedback on acceptable sound levels for quiet supersonic flight.
Through Quesst’s development of the X-59, NASA will deliver design tools and technology for quiet supersonic airliners that will achieve the high speeds desired by commercial operators without creating disturbance to people on the ground.
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Last Updated Jul 11, 2025 EditorLillian GipsonContactJim Bankejim.banke@nasa.gov Related Terms
Aeronautics Aeronautics Research Mission Directorate Low Boom Flight Demonstrator Quesst (X-59) Quesst: The Vehicle Supersonic Flight View the full article
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By NASA
2 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA’s Sustainable Flight Demonstrator project concluded wind tunnel testing in the fall of 2024. Tests on a Boeing-built X-66 model were completed at NASA’s Ames Research Center in California’s Silicon Valley in its 11-Foot Transonic Unitary Plan Facility. The model underwent tests representing expected flight conditions to obtain engineering information to influence design of the wing and provide data for flight simulators.NASA/Brandon Torres Navarrete NASA’s Sustainable Flight Demonstrator (SFD) project recently concluded wind tunnel tests of its X-66 semi-span model in partnership with Boeing. The model, designed to represent half the aircraft, allows the research team to generate high-quality data about the aerodynamic forces that would affect the actual X-66.
Test results will help researchers identify areas where they can refine the X-66 design – potentially reducing drag, enhancing fuel efficiency, or adjusting the vehicle shape for better flying qualities.
Tests on the Boeing-built X-66 semi-span model were completed at NASA’s Ames Research Center in California’s Silicon Valley in its 11-Foot Transonic Unitary Plan Facility. The model underwent tests representing expected flight conditions so the team could obtain engineering information to influence the design of the aircraft’s wing and provide data for flight simulators.
NASA’s Sustainable Flight Demonstrator project concluded wind tunnel testing in the fall of 2024. Tests on a Boeing-built X-66 model were completed at NASA’s Ames Research Center in California’s Silicon Valley in its 11-Foot Transonic Unitary Plan Facility. Pressure points, which are drilled holes with data sensors attached, are installed along the edge of the wing and allow engineers to understand the characteristics of airflow and will influence the final design of the wing.NASA/Brandon Torres Navarrete Semi-span tests take advantage of symmetry. The forces and behaviors on a model of half an aircraft mirror those on the other half. By using a larger half of the model, engineers increase the number of surface pressure measurements. Various sensors were placed on the wing to measure forces and movements to calculate lift, drag, stability, and other important characteristics.
The semi-span tests follow earlier wind tunnel work at NASA’s Langley Research Center in Hampton, Virginia, using a smaller model of the entire aircraft. Engineers will study the data from all of the X-66 wind tunnel tests to determine any design changes that should be made before fabrication begins on the wing that will be used on the X-66 itself.
The SFD project is NASA’s effort to develop more efficient aircraft configurations as the nation moves toward aviation that’s more economically, societally, and environmentally sustainable. The project seeks to provide information to inform the next generation of single-aisle airliners, the most common aircraft in commercial aviation fleets around the world. Boeing and NASA are partnering to develop the X-66 experimental demonstrator aircraft.
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Last Updated Feb 05, 2025 EditorDede DiniusContactSarah Mannsarah.mann@nasa.govLocationArmstrong Flight Research Center Related Terms
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By NASA
Climate change presents one of the most urgent crises of our time, with increasing threats to life, infrastructure, economies, and ecosystems worldwide. Climate change is no longer a distant concern; its effects are being felt now and are projected to intensify if emissions continue unabated. The consequences are severe and irreversible for people today, with rapidly shrinking glaciers and ice sheets, rising sea levels, and more intense heat waves already occurring. Scientists predict even more profound impacts, such as an increase in the frequency and intensity of wildfires, extended drought periods, and stronger tropical cyclones. By 2100, sea levels could rise by up to 6.5 feet1, displacing coastal communities and disrupting ecosystems. In the U.S., the effects vary by region—wildfires in the West have doubled in area burned, and rising sea levels threaten infrastructure in the Southeast. Innovative, data-driven solutions are essential to mitigate these growing risks. From the unique vantage point in space, NASA collects critical long-term observations of our changing planet. NASA produces vast amounts of Earth system science data from satellites, radars, and ships, as well as model outputs, offering a wealth of opportunities for innovative thinkers to leverage these sources. The Sustainable Business Model Challenge is designed to identify and foster sustainable business models built around NASA’s Earth system science data. This challenge invites entrepreneurs, researchers, startups, and innovators to use NASA’s publicly available climate and Earth system data sources to create sustainable business models to address climate challenges.
Award: $100,000 in total prizes
Open Date: January 16, 2025
Close Date: June 13, 2025
For more information, visit: https://nasabusinesschallenge.org/
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By NASA
At Goddard Space Flight Center, the GSFC Data Science Group has completed the testing for their SatVision Top-of-Atmosphere (TOA) Foundation Model, a geospatial foundation model for coarse-resolution all-sky remote sensing imagery. The team, comprised of Mark Carroll, Caleb Spradlin, Jordan Caraballo-Vega, Jian Li, Jie Gong, and Paul Montesano, has now released their model for wide application in science investigations.
Foundation models can transform the landscape of remote sensing (RS) data analysis by enabling the pre-training of large computer-vision models on vast amounts of remote sensing data. These models can be fine-tuned with small amounts of labeled training and applied to various mapping and monitoring applications. Because most existing foundation models are trained solely on cloud-free satellite imagery, they are limited to applications of land surface or require atmospheric corrections. SatVision-TOA is trained on all-sky conditions which enables applications involving atmospheric variables (e.g., cloud or aerosol).
SatVision TOA is a 3 billion parameter model trained on 100 million images from Moderate Resolution Imaging Spectroradiometer (MODIS). This is, to our knowledge, the largest foundation model trained solely on satellite remote sensing imagery. By including “all-sky” conditions during pre-training, the team incorporated a range of cloud conditions often excluded in traditional modeling. This enables 3D cloud reconstruction and cloud modeling in support of Earth and climate science, offering significant enhancement for large-scale earth observation workflows.
With an adaptable and scalable model design, SatVision-TOA can unify diverse Earth observation datasets and reduce dependency on task-specific models. SatVision-TOA leverages one of the largest public datasets to capture global contexts and robust features. The model could have broad applications for investigating spectrometer data, including MODIS, VIIRS, and GOES-ABI. The team believes this will enable transformative advancements in atmospheric science, cloud structure analysis, and Earth system modeling.
The model architecture and model weights are available on GitHub and Hugging Face, respectively. For more information, including a detailed user guide, see the associated white paper: SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery.
Examples of image reconstruction by SatVision-TOA. Left: MOD021KM v6.1 cropped image chip using MODIS bands [1, 3, 2]. Middle: The same images with randomly applied 8×8 mask patches, masking 60% of the original image. Right: The reconstructed images produced by the model, along with their respective Structural Similarity Index Measure (SSIM) scores. These examples illustrate the model’s ability to preserve structural detail and reconstruct heterogeneous features, such as cloud textures and land-cover transitions, with high fidelity.NASAView the full article
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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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