GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: MAVEN observation of Kelvin-Helmholtz Vortices using Statistical and Machine Learning Techniques
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: MAVEN observation of Kelvin-Helmholtz Vortices using Statistical and Machine Learning Techniques
Purpose and benefits: MAVEN observation of Kelvin-Helmholtz Vortices using Statistical and Machine Learning Techniques
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Message Anomaly Detection for Command and Telemetry Systems (MADCAT)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Message Anomaly Detection for Command and Telemetry Systems (MADCAT)
Purpose and benefits: Message Anomaly Detection for Command and Telemetry Systems (MADCAT)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Modeling NICER background
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Modeling NICER background
Purpose and benefits: Modeling NICER background
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NAMASTE
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: NAMASTE
Purpose and benefits: NAMASTE
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NASA Evolutionary Programming Analytic Center (NEPAC)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: NASA Evolutionary Programming Analytic Center (NEPAC)
Purpose and benefits: NASA Evolutionary Programming Analytic Center (NEPAC)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: National Forest Mapping Old Growth Forest
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: National Forest Mapping Old Growth Forest
Purpose and benefits: National Forest Mapping Old Growth Forest
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Natural Language Processing and Topic Modeling of the 25-Year General Coordinates Network (GCN) Circulars Archive
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Natural Language Processing and Topic Modeling of the 25-Year General Coordinates Network (GCN) Circulars Archive
Purpose and benefits: Natural Language Processing and Topic Modeling of the 25-Year General Coordinates Network (GCN) Circulars Archive
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Navajo Nation Feature Mapping
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Navajo Nation Feature Mapping
Purpose and benefits: Navajo Nation Feature Mapping
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Neural Network based observation operator to bias correct SMAP brightness temperatures for assimilation
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Neural Network based observation operator to bias correct SMAP brightness temperatures for assimilation
Purpose and benefits: Neural Network based observation operator to bias correct SMAP brightness temperatures for assimilation
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Neutron star synthetic wave form generation
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Neutron star synthetic wave form generation
Purpose and benefits: Neutron star synthetic wave form generation
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Next generation cloud and aerosol parameterizations for atmospheric models
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Next generation cloud and aerosol parameterizations for atmospheric models
Purpose and benefits: Next generation cloud and aerosol parameterizations for atmospheric models
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Nitrogen dioxide retrieval using hyper-spectral imagers
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Nitrogen dioxide retrieval using hyper-spectral imagers
Purpose and benefits: Nitrogen dioxide retrieval using hyper-spectral imagers
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Physics of neural network power-law scaling
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Physics of neural network power-law scaling
Purpose and benefits: Physics of neural network power-law scaling
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Artificial Intelligent Co-Processor Slice with Google Coral TPU For Automated Onboard Data-Product Generation
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The Objective of this proposal is to enable the use of state-of-the-art, experimental, artificial-intelligent (AI) microchip architectures such as the Google Coral TPU (Tensor Processing Unit) on a SmallSat platform.
Purpose and benefits: Artificial Intelligent Co-Processor Slice with Google Coral TPU For Automated Onboard Data-Product Generation
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Planetary Trajectory Design Using Generative AI Tools
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Planetary Trajectory Design Using Generative AI Tools
Purpose and benefits: Planetary Trajectory Design Using Generative AI Tools
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Planted Area mapping in food insecure conflict zones
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Planted Area mapping in food insecure conflict zones
Purpose and benefits: Planted Area mapping in food insecure conflict zones
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PM2.5 Estimation using MERRA2 and Advanced machine learning model over US
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: PM2.5 Estimation using MERRA2 and Advanced machine learning model over US
Purpose and benefits: PM2.5 Estimation using MERRA2 and Advanced machine learning model over US
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PM2.5 Product development using AERONET data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: PM2.5 Product development using AERONET data
Purpose and benefits: PM2.5 Product development using AERONET data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Prediction of Spaceflight Mass Spectrometry Chemical Information with Neural Networks
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Prediction of Spaceflight Mass Spectrometry Chemical Information with Neural Networks
Purpose and benefits: Prediction of Spaceflight Mass Spectrometry Chemical Information with Neural Networks
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Prediction of whitecaps
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Prediction of whitecaps
Purpose and benefits: Prediction of whitecaps
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Quantifying Uncertainty and Constraining Parameterizations of Clouds in Earth System Models using NASA Observations
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Quantifying Uncertainty and Constraining Parameterizations of Clouds in Earth System Models using NASA Observations
Purpose and benefits: Quantifying Uncertainty and Constraining Parameterizations of Clouds in Earth System Models using NASA Observations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Rangelands Water Monitoring and Forecasting System
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Rangelands Water Monitoring and Forecasting System
Purpose and benefits: Rangelands Water Monitoring and Forecasting System
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Reconstruction of VIIRS Level1 geolocation data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Reconstruction of VIIRS Level1 geolocation data
Purpose and benefits: Reconstruction of VIIRS Level1 geolocation data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Reproducing surface irradiance and penetration depth retrievals using machine learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Reproducing surface irradiance and penetration depth retrievals using machine learning
Purpose and benefits: Reproducing surface irradiance and penetration depth retrievals using machine learning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: RST I&T Science Data Telemetry Query
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: RST I&T Science Data Telemetry Query
Purpose and benefits: RST I&T Science Data Telemetry Query
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: RST Image Anomaly Detection
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: RST Image Anomaly Detection
Purpose and benefits: RST Image Anomaly Detection
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SatVision: Precursor for a foundation model developed using MODIS surface reflectance data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: SatVision: Precursor for a foundation model developed using MODIS surface reflectance data
Purpose and benefits: SatVision: Precursor for a foundation model developed using MODIS surface reflectance data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Science Autonomy Applications for ExoMars/MOMA
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Science Autonomy Applications for ExoMars/MOMA
Purpose and benefits: Science Autonomy Applications for ExoMars/MOMA
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Science Keyword Link Prediction
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Science Keyword Link Prediction
Purpose and benefits: Science Keyword Link Prediction
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Self-supervised learning for modeling gamma-ray variability in blazars
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Self-supervised learning for modeling gamma-ray variability in blazars
Purpose and benefits: Self-supervised learning for modeling gamma-ray variability in blazars
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Smart NINT
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Smart NINT
Purpose and benefits: Smart NINT
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Software Issue Classification with LLM
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Software Issue Classification with LLM
Purpose and benefits: Software Issue Classification with LLM
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: SpRInT to Advance the SOA of Intelligent Space Systems
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: SpRInT to Advance the SOA of Intelligent Space Systems
Purpose and benefits: SpRInT to Advance the SOA of Intelligent Space Systems
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Super Resolution to enhance climate reanalysis data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Super Resolution to enhance climate reanalysis data
Purpose and benefits: Super Resolution to enhance climate reanalysis data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Super-Resolution for Nighttime Lights
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Super-Resolution for Nighttime Lights
Purpose and benefits: Super-Resolution for Nighttime Lights
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Terrain Modeling and Landmark Navigation with Radiance Fields
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Terrain Modeling and Landmark Navigation with Radiance Fields
Purpose and benefits: Terrain Modeling and Landmark Navigation with Radiance Fields
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Terrestrial Environmental Rapid-Replicating and Assimilation Hydrometeorological (TERRAHydro) System
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Terrestrial Environmental Rapid-Replicating and Assimilation Hydrometeorological (TERRAHydro) System
Purpose and benefits: Terrestrial Environmental Rapid-Replicating and Assimilation Hydrometeorological (TERRAHydro) System
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Text to Spacecraft
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Text to Spacecraft
Purpose and benefits: Text to Spacecraft
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: The evaluation of clouds in R21C data via a ML-based MODIS simulator
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The evaluation of clouds in R21C data via a ML-based MODIS simulator
Purpose and benefits: The evaluation of clouds in R21C data via a ML-based MODIS simulator
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Toward ice sheet surface data assimilation: Employing satellite observations and machine learning to improve model representation of ice sheet surface melt
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Identify sources of error in an Earth system model component.
Purpose and benefits: Improve the representation of the Earth system in diagnostic model simulations, data assimilation, and predictions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Towards an Integrated Observation System for OH: A Case Study in the Tropics
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Towards Learning-based Visual Perception with GAVIN: the Goddard AI Verification and INtegration Tool Suite
Purpose and benefits: Towards an Integrated Observation System for OH: A Case Study in the Tropics
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Towards Learning-based Visual Perception with GAVIN: the Goddard AI Verification and INtegration Tool Suite
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Towards Learning-based Visual Perception with GAVIN: the Goddard AI Verification and INtegration Tool Suite
Purpose and benefits: Towards Learning-based Visual Perception with GAVIN: the Goddard AI Verification and INtegration Tool Suite
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Depth from Spectral Defocus for Lunar Regolith
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Our technique reconstructs a 3D model of a scene based on images taken from a single viewpoint -- via estimating a depth map of a scene based on the defocus of different objects in captured images-- illuminated with light of different wavelengths. This pipeline has potential applications for 3D reconstruction on upcoming lunar missions carrying a multi-spectral imaging system as payload.
Purpose and benefits: Our pipeline has shown success on synthetic lunar data sets taken with the Ames Imaging Module (NIRVSS-AIM) camera system.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Neural Scene Representations for Lunar Terrain Modeling
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: We have developed LunarNRM, a novel neural surface reconstruction algorithm based on Neural Radiance Fields (NeRFs) that incorporates shadow-aware and depth-aware methodologies.
Purpose and benefits: By integrating multi-sensor data from the Lunar Reconnaissance Orbiter (LRO)—specifically optical data from the Narrow Angle Camera (NAC) and altimeter data from the Lunar Orbiter Laser Altimeter (LOLA)—we have demonstrated that LunarNRM can effectively reconstruct these critical regions, directly supporting NASA’s Artemis campaign.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Data Augmentation Pipeline for Zero-Shot Sim-to-Real Transfer in Vision-Based Robot Navigation
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: We developed a data augmentation pipeline to enhance the training of vision-based navigation models for robotics, addressing the challenges of limited real-world data.
Purpose and benefits: By combining foundation model-based segmentation with CycleGAN for sim-to-real style transfer, our approach generates realistic, labeled images from synthetic data, enabling models to better generalize to real-world environments. This innovative capability enhances vision-based navigation tasks, such as vehicle pose estimation and road segmentation, significantly closing the sim-to-real gap in robotic perception applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Safe Autonomous Taxiing with Vision-Based Navigation
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: TaxiNet is a vision-based deep learning model developed to enable autonomous vehicles to follow a designated line safely during aircraft taxiing, a crucial application for assured autonomy research. This project’s goal is to demonstrate safe, closed-loop control using neural network perception from camera images, focusing on meeting rigorous safety standards for learning-enabled components (LECs) in safety-critical contexts.
Purpose and benefits: TaxiNet is a vision-based deep learning model developed to enable autonomous vehicles to follow a designated line safely during aircraft taxiing, a crucial application for assured autonomy research. This project’s goal is to demonstrate safe, closed-loop control using neural network perception from camera images, focusing on meeting rigorous safety standards for learning-enabled components (LECs) in safety-critical contexts.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Global, Seasonal Mars Frost Maps
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Global frost Martian maps derived from five remote sensing datasets and processed with tools like CNNs and other data science techniques. Publicly available on JMARS
Purpose and benefits: (1) Mars seasonal frost maps that can be used by other researchers, enabling more time on analysis rather than data collection. (2) Demonstration of a deeply collaborative effort between data scientists and physical scientists.
Topic area: Science
Development stage: Deployed
First used: 2024-11-01
US federal AI use case: GenAI Agent for Interacting with Robots (https://github.com/nasa-jpl/rosa)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: ROSA is an AI agent that integrates with the ROS ecosystem to help develop and operate robots using natural language. It is able to read live telemetry data from robotics systems and formulate answers to user queries.
Purpose and benefits: ROSA is an AI agent that integrates with the ROS ecosystem to help develop and operate robots using natural language. It is able to read live telemetry data from robotics systems and formulate answers to user queries.
Topic area: Science
Development stage: Pilot
First used: 2023-08-01
US federal AI use case: SLIM: Software Lifecycle Improvement & Modernization
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Use GenAI to help automate integration of software best practices, including adding appropriate documents, testing plans, tests, etc.
Purpose and benefits: Use GenAI to help automate integration of software best practices, including adding appropriate documents, testing plans, tests, etc.
Topic area: IT
Development stage: Pilot
First used: 2022-02-01
US federal AI use case: Using LLMS for Documents and Requirements Analysis
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Use GenAI to analyze documents and improve the requirements development process
Purpose and benefits: Use GenAI to analyze documents and improve the requirements development process
Topic area: Administrative functions
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Purchase Card Management System (PCMS)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Purchase card application uses ML model to suggest if a purchase may be a taggable asset or a chemical.
Purpose and benefits: Purchase card application uses ML model to suggest if a purchase may be a taggable asset or a chemical.
Topic area: Administrative functions
Development stage: Deployed
First used: 2018-11-15
US federal AI use case: New Technology and Software Reporting (NTR)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: NTR application uses ML model to suggest a Technology Category (e.g. Aerospace, Robotics) for the new technology being reported
Purpose and benefits: NTR application uses ML model to suggest a Technology Category (e.g. Aerospace, Robotics) for the new technology being reported
Topic area: Administrative functions
Development stage: Deployed
First used: 2024-08-22
US federal AI use case: Artificial Intelligence Leveraged Information Capture and Exploration (ALICE) (ITMX)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: The project will streamline the archiving of information and build an explorable knowledge graph of organizational communications. This project seeks to provide several significant benefits to the agency.
Purpose and benefits: The project will streamline the archiving of information and build an explorable knowledge graph of organizational communications. This project seeks to provide several significant benefits to the agency.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: TxP: Artificial Intelligence for Curation (AI-Cure)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: The AI-CURE project will integrate advanced AI and machine learning models to automate and standardize data curation across NASA’s SMD databases.
Purpose and benefits: This will result in cost savings, faster data availability, improved data quality, and better support for NASA’s open science initiatives. This project enables NASA to advance data curation processes, accelerating scientific discovery while reducing manual effort and operational costs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: TxP: Federated Data Discovery and Content Creation (FDDCC) KDP-Formulation (ITMX)
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: The FDDCC project is guided by a set of core objectives aimed at transforming how data is accessed, managed, and utilized across the agency. These objectives focus on enhancing data discoverability, ensuring secure access, improving system scalability, and fostering a user-centered experience.
Purpose and benefits: NASA has amassed a significant trove of data that holds the potential to drive advancements across numerous domains. However, the organization faces significant challenges in harnessing this potential. Much of its valuable data remains trapped within isolated legacy systems, creating silos that hinder efficient access and use. This segmentation is further complicated by the dual locations of these systems – both in the cloud and on-premises – with each presenting its unique set of access challenges due to firewalls, security control boundaries and other barriers. Numerous distributed systems within NASA provide localized search functions for their specific datasets. These capabilities, which cater to both structured and unstructured data, highlight the latent potential and need for a comprehensive approach that can unify and streamline data and information access across the entire organization. By developing a unified search interface, implementing robust identity and access management, creating a comprehensive data catalog, and building a scalable infrastructure, the FDDCC project seeks to empower users to efficiently find, access, and manage data. Additionally, the project emphasizes user satisfaction through continuous feedback and iterative improvements, ensuring that the platform meets the evolving needs of the agency.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Fast Machine Learning Lidar Surrogate Simulator: For Pristine Clear Sky
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: ML base lidar radiative transfer simulation for clear sky
Purpose and benefits: ML base lidar radiative transfer simulation for clear sky
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Cloud Parameterization to improve climate model
Purpose and benefits: Cloud Parameterization to improve climate model
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Intelligent Chatbot for Science using Microsoft Copilot
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: It uses Microsoft's Large Language model with scientifically curated information from NASA's VEDA (Visualization, Exploration, and Data Analysis) platform to assist users in search, discovery and analysis.
Purpose and benefits: It uses Microsoft's Large Language model with scientifically curated information from NASA's VEDA (Visualization, Exploration, and Data Analysis) platform to assist users in search, discovery and analysis.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Cache-Augmented Generation Document Search (converted to collective)
Agency: National Aeronautics and Space Administration - WS: White Sands Test Facility
Problem addressed: Cache-Augmented Generation Document Search
Purpose and benefits: Cache-Augmented Generation Document Search
Topic area: Science
Development stage: Retired
US federal AI use case: Personal Health Information (PHI) AI Platform
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Ability to use PHI data
Purpose and benefits: Ability to use PHI data
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Entity Matching
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: This AI use case addresses a critical data quality challenge within NASA’s enterprise Salesforce platform by enabling NASA to accurately merge extremely large institutional datasets from multiple sources into a single, unified system, while identifying and preventing duplicate account entries that result from inconsistencies in naming, formatting, and metadata. With these inconsistencies across sources, traditional matching methods are insufficient to maintain a clean and reliable master list of organizations. By implementing AI-assisted entity matching and de-duplication, NASA can ensure accurate, non-redundant records that enhance reporting, support seamless user experiences, and enable the agency to scale its engagement infrastructure without compromising data integrity.
Purpose and benefits: STMD is advancing this initiative in support of its broader effort to modernize how NASA collaborates with academia, industry, and government entities. Given the large number of individuals and organizations seeking to engage with NASA, it is essential that institutional records remain clean, traceable, and well-governed. This use case directly enhances STMD’s ability to manage an authoritative system of record that supports outreach, partnership tracking, and strategic engagement. By implementing AI-driven matching and de-duplication, STMD is not only enabling faster and more reliable onboarding of new institutional data, but also delivering enterprise-wide value by improving data integrity across all applications built on the shared Salesforce platform. These improvements will ensure that external users experience a seamless, accurate interface when associating with their organization—while internal teams gain more trustworthy data for programmatic planning, performance tracking, and cross-agency collaboration.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Automated Data Normalization for Institutional Records
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: This AI use case aims to automate the cleanup, transformation, and normalization of large institutional datasets that feed into NASA’s enterprise Salesforce platform. Currently, preparing these records requires time-consuming manual work to merge and standardize disparate data sources before they can be reliably used in NASA systems. By leveraging AI and machine learning, this solution will drastically reduce manual effort, improve data accuracy, and ensure a consistent, high-quality institutional dataset that supports external engagement, enterprise reporting, and mission readiness across the agency.
Purpose and benefits: STMD has identified a strategic requirement to modernize how the agency tracks and manages interactions with academia and industry in order to promote increased collaboration, expand engagement across the innovation ecosystem, and accelerate the maturation of strategic partnerships. By automating this critical data pipeline, STMD is enabling a more connected, data-driven infrastructure that not only supports its own mission but also delivers agency-wide benefits through improved data accuracy, consistency, and accessibility across all enterprise applications within the platform. End users will benefit from a more robust, reliable, and searchable system of record, making it easier to find and associate with the correct organizations during their interactions with NASA.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Automating Data Stewardship for Temporary Account Reconciliation
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: This AI use case focuses on automating the manual review and verification process for temporary “write-in” accounts submitted by external users within NASA’s agency-wide enterprise Salesforce platform. When users cannot find their organization in the existing institutional database, they submit temporary account records that require data stewards to manually research, validate, and reconcile each entry—often spending up to 50 hours per week. By leveraging AI-driven entity matching, web verification, and metadata enrichment, this solution will significantly reduce manual effort, accelerate the onboarding of new organizations, and improve data accuracy. Automating this process enhances scalability, ensures compliance with data governance policies, and improves the end-user experience across all applications using the enterprise platform.
Purpose and benefits: Given the large number of individuals and institutions that show interest in collaborating with NASA, we must ensure that records associated with specific entities remain clean, accurate, and traceable in order to enhance our ability to manage partnerships, track engagement trends, and support strategic outreach across the agency. STMD is championing this automation effort to streamline the reconciliation of temporary accounts, which is essential for maintaining the integrity of NASA’s enterprise-wide contact and organization data. This work not only supports STMD’s mission to accelerate the development and adoption of transformative space technologies through broader collaboration but also strengthens the agency’s ability to scale engagement efficiently and responsibly across academia, industry, and government.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Ask Tech Port
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: Ongoing development and enhancement of the NASA Technology Portfolio Management System (TechPort) "AskTechPort" generative AI tool. AskTechPort leverages the TechPort data set of over 18,000 current and historical NASA applied research and experimental development investments to answer questions about NASA's technology portfolio and capabilities. AskTechPort provides an interactive feature enabling users to inquire about NASA technology investment areas, maturity of those technologies, organizations developing those technologies, and where work is being performed.
Purpose and benefits: TechPort sees an average of over 12,000 unique users each month. These users conduct over 250,000 searches each year of the NASA technology portfolio. The benefit of "Ask Tech Port" is a component that reduces the total time to search or retrieve the results of a data inquiry by 50%, providing interactive features that use natural language processing to provide a faster inquiry mechanism than historical faceted search techniques.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Enhancements to T-Rex and D-Rex
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: Enhancement and further training of the NASA Technology Taxonomy and Target Technology Destination recommendation systems, known as T-Rex and D-Rex respectively. T-Rex and D-Rex are predictive AI models residing in the NASA TechPort system that help technologists more efficiently categorize technology using the NASA Technology Taxonomy and Target Technology Destinations. These models help ensure completeness of the categorization of technologies in the portfolio, and are used as verification and validation tools.
Purpose and benefits: The NASA TechPort system catalogs over 2,000 technology investments made by the Agency each year. Technologists spend a significant amount of time manually categorizing technology project investments according to the NASA Technology Taxonomy and Target Technology Destinations. The benefit to the american public is improved model accuracy by more than 8% and the saving of over 330 hours of manual labor each year.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: AI-enabled Risk-Informed Assessment Selection (RIAS) tool
Agency: National Aeronautics and Space Administration - OSMA: Office of the Chief Safety & Mission Assurance
Problem addressed: This project leverages LLM-based tools to automate data ingestion, risk trend detection and scoring, and compliance determination across a variety of NASA applicable data. The innovative approach aims to streamline risk identification and prioritization while positioning our team at the forefront of AI-enabled risk management practices.
Purpose and benefits: Leverage a wide range of data to identify risk trends and prioritize areas for assessment and risk reduction.
Topic area: Other (use other text field)
Development stage: Pilot
US federal AI use case: AI-Assisted Analysis Across the IV&V and Software Assurance Lifecycle
Agency: National Aeronautics and Space Administration - OSMA: Office of the Chief Safety & Mission Assurance
Problem addressed: Leverage opportunities to automate overly manual analysis tasks, with a goal of amplifying productiivity and efficiency, allowing employees to free up their time for more complex tasks.
Purpose and benefits: Reduce false positives in static analysis, trace design to code, identify issues, generate comments, requirements/design traceability, gap detection, change impact analysis, completeness/correctness checks, Generate edge/off-nominal test cases, identify missing tests, assess requirement flow-down..
Topic area: Other (use other text field)
Development stage: Pre-deployment
US federal AI use case: Module for Event Driven Operations on Spacecraft (MEDOS)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: MEDOS, a flight-tested onboard decision engine
Purpose and benefits: Event Driven operations (MEDOS) is agentic-focused, requiring little to no training data (compared with ML and RL methods), instead focusing on using existing subject matter expertise in complement with state-of-the-art intelligent systems.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Remote Coordination, Actuation, and Planning (ReCAP) Cooperative MultiAgent Architecture
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: ReCAP provides an architecture for lightweight, efficient coordination of highly-capable agents in a comms-limited environment. By providing agents with high-level, lightweight information about the capabilities of other agents in the fleet, ReCAP can facilitate allocation of tasks that are dynamically discovered and assigned via an auction. Auctions are conducted for particular tasks, and agents formulate bids based on encoded subject matter expertise predicting how a given action will affect their operations. For example, one agent may request that two others bid on a sampling task. The two agents bidding may possess different instruments for sampling, one of them able to sample remotely, but at low fidelity, the other able to sample at high fidelity but requiring a physical sample be collected. The auctioneer agent will consider its own mission goals and award the task to what it deems to be the highest bid. Onboard single agents, ReCAP provides a planning/scheduling module and a behavior tree for real-time control. The planning/scheduling module includes the actions of other connected agents, so that any agent may form a plan sequence involving actions from multiple agents – this is what triggers an auction. Finally, the actions that can be executed onboard are handled by a behavior tree, which is more reactive and requires less frequent reconfiguration than a static planner.
Purpose and benefits: Compared to existing multi-agent control systems, ReCAP requires no training data, before or during operations, and does not require centralized control. It was conceived alongside a NASA push for extensible mission architectures, intended to provide technology that would allow missions to interoperate as new assets are added or encountered, and to enable on-demand coordination between otherwise separate missions. ReCAP enables this capability by basing coordination on high-level, lightweight communications, and its auctioning system prevents the need for interchange of complex state information between agents. ReCAP has been deployed to success in field, laboratory, and simulated use cases across terrestrial and space applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Intelligent Camera Analytics for Security and Operations
Agency: National Aeronautics and Space Administration - OPS: Office of Protective Services
Problem addressed: Traditional video surveillance requires continuous human monitoring, which is resource-intensive and prone to missed events. By applying AI-driven camera analytics, this use case addresses the need for faster, more reliable detection of security incidents and operational insights. The purpose is to enhance situational awareness, reduce manual effort, and improve response times while also enabling organizations to repurpose video data for business and safety improvements.
Purpose and benefits: By leveraging AI-driven video surveillance, organizations can significantly improve efficiency and security outcomes. Automated monitoring reduces the need for constant human oversight, lowering operational costs while also decreasing the likelihood of missed incidents. Faster and more accurate detection of anomalies—such as unauthorized access, unattended objects, or unusual behavior—enhances safety for personnel and protects critical assets. In addition, video analytics provide valuable secondary benefits, including occupancy tracking, traffic flow analysis, and facility utilization insights that support data-driven decision-making. These capabilities enable scalability without requiring proportional increases in staffing, delivering measurable return on investment through reduced false alarms, quicker response times, improved compliance with safety protocols, and optimized use of resources.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2016-01-01
US federal AI use case: Space Precision Health Foundation Model
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: This project aims to leverage data available within the NASA Open Science Data Repository to train a model or set of models that can predict patterns related space health outcomes.
Purpose and benefits: These models developed will enable the scientific community to better understand how biological systems respond to spaceflight stressors and inform mission planners.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Foundation Model for Lunar Science
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: The purpose  of Lunar FM is to overcome the limitations of traditional, task-specific Machine Learning (ML) models in analyzing the vast, diverse, and long-term datasets collected by the Lunar Reconnaissance Orbiter (LRO) mission. This includes addressing data challenges such as heterogeneity in spatial and temporal resolution, differences in data formats and calibration standards, and the significant lack of large, accurately labeled datasets (sparse ground truth) for supervised learning.
Purpose and benefits: The LRO Foundation Model (FM) is useful because it offers key advantages over traditional ML:
Generalization: It can learn general-purpose representations that generalize better across different tasks and even data from different instruments with minimal fine-tuning.
Low-Data Regimes: It excels in transfer learning, allowing knowledge from pretraining on massive unlabeled datasets to be effectively applied to downstream tasks that only have limited labeled data.
Efficiency: It significantly reduces the dependence on time-consuming manual data labeling by using self-supervised pretraining on vast amounts of unlabeled LRO data.
Scientific Advancement: It enables understanding of lunar features by jointly analyzing multiple data modalities, leading to new scientific insights and supporting mission-critical activities like landing site selection.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: JSC NC PRA Cut Set Tool
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Probabilistic Risk Assessment (PRA) identifies minimal cut sets - smallest combination of simultaneous failures to cause a system or mission failure.
Purpose and benefits: Probabilistic Risk Assessment (PRA) identifies minimal cut sets - smallest combination of simultaneous failures to cause a system or mission failure.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: JSC SMA NT Paper WAD Data Extraction
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: This application aims to provide the Quality Flight Equipment Division with a strong paper Work Authorization Document (WAD) data extraction capability using AI models. The data inputs include scanned PDF versions of original paper Work Authorization Documents (WADs) stored in the QFED data center. This application will be leveraging Google's Gemini Pro models to accomplish this effort. Users will be able to query critical quality-based information from large numbers of WADs to better understand Task Performance Sheet and Discrepancy Report execution trends and areas for improvement.
Purpose and benefits: This application aims to provide the Quality Flight Equipment Division with a strong paper Work Authorization Document (WAD) data extraction capability using AI models. The data inputs include scanned PDF versions of original paper Work Authorization Documents (WADs) stored in the QFED data center. This application will be leveraging Google's Gemini Pro models to accomplish this effort. Users will be able to query critical quality-based information from large numbers of WADs to better understand Task Performance Sheet and Discrepancy Report execution trends and areas for improvement.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI Tool for Automatic Identification of Soliton Features in SWOT data
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Automatically detect features of Soliton for scientists to study the ocean.
Purpose and benefits: Must be faster than manual analysis of the SWOT data.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI Based Digital Twin Interoporability Schema
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Using IEEE 2874 Spatial Web standard to implement a new Hyperspace Modeling Language (HSML) and Hyperspace Transaction Protocol (HSTP) to establish communications among heterogeneous simulation platforms.
Purpose and benefits: Making collaboration of digital twins possible in different platforms.
Topic area: Science
Development stage: Pilot