GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Friction Stir Weld Control
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: The AI agent monitors the telemetry and supplements the human controlling the Self-Reacting Friction Stir Weld by sending updated trim commands to fine tune the weld parameters to keep the calculated power within the researched and specified band as conditions change.
Purpose and benefits: The SR-FSW has a lower defect rate reducing rework, cost, and number of test welds, providing more resilient structures.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI Digital Assistants (HRP) [was "Doc in a Box" for Earth Independent Medical Operations (EIMO)]
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: A software agent that can perform tasks or services for an individual based on commands or questions. These commands can be given through text, voice, or other interfaces. DAs leverage artificial intelligence (AI), natural language processing (NLP) and machine learning (ML) to understand user requests, learn from interactions and provide increasingly personalized and accurate responses.
Purpose and benefits: Improved cost savings, time savings, and process improvement in flight-related procedures related to medical conditions during flight.
Topic area: Health and Medical
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: Digital assistant does not take any action, only gives information for a crew to act upon. Informational purposes only.
First used: 2024-10-01
US federal AI use case: Data Fracking (HRP)
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Software systems and methods that leverage artificial intelligence methodologies to automate, enhance and streamline various processes related to documents. The core purpose of an AI document solution is to make document-driven tasks more efficient and accurate, while also providing valuable insights, all by minimizing manual labor and applying sophisticated analytical capabilities.
Purpose and benefits: Improved cost savings, time savings, and process improvement in daily tasks related to scientific research.
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: AIML for Code Review and Workforce Augmentation (converted to collective)
Agency: National Aeronautics and Space Administration - WS: White Sands Test Facility
Problem addressed: AI Studio API to GIT type Repositories for Code Review, Debating Agent implementation, and automation.
Purpose and benefits: AI Studio API to GIT type Repositories for Code Review, Debating Agent implementation, and automation.
Topic area: Science
Development stage: Retired
US federal AI use case: OCFO External Performance Reporting
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: Gen AI uses performance data provided by MDs to write a draft summary performance report
Purpose and benefits: Gen AI uses performance data provided by MDs to write a draft summary performance report
Topic area: Administrative functions
Development stage: Pre-deployment
US federal AI use case: Reinforcement Learning for Lunar Lander Real Time Optimal Trajectory
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: The proposal's objective is to apply RL for real-time optimal guidance and control in spacecraft systems, aiming to enhance efficiency, robustness, and autonomy.
Purpose and benefits: The proposal's objective is to apply RL for real-time optimal guidance and control in spacecraft systems, aiming to enhance efficiency, robustness, and autonomy.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: OCFO Credit Risk and Defaul Estimation Tool
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: Machine learning based estimation tool for company default risk
Purpose and benefits: Machine learning based estimation tool for company default risk
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: OCFO President Budget Request RAG
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: A retrieval augmented generation tool that can answer questions based on president's budget requests
Purpose and benefits: A retrieval augmented generation tool that can answer questions based on president's budget requests
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Borescope Artificial Intelligence (BAI)
Agency: National Aeronautics and Space Administration - AFRC: Armstrong Flight Research Center
Problem addressed: The BAI will aid jet engine maintainers in identifying compressor and turbine blade defects
Purpose and benefits: Advanced borescopes would lead to an increased defect capture rate in less maintenance time, keeping engines on wing longer with more confidence in their health.
Topic area: Other (use other text field)
Development stage: Pre-deployment
US federal AI use case: Coupled Loads Analysis Research Assistant (CLARA)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: chat assistant to support loads analysts during computation.
Purpose and benefits: time savings for loads analysts
Topic area: IT
Development stage: Retired
US federal AI use case: Autoresolver/Tailored Arrival Manager
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The Autoresolver system is a tool for autonomous air traffic management. It is designed to perform many of the tasks that air-traffic controllers have historically performed including maintaining separation between aircraft, sequencing and scheduling aircraft across locations in space, and avoiding airspace volumes like weather systems and restricted airspace.
Purpose and benefits: The Autoresolver system is a tool for autonomous air traffic management. It is designed to perform many of the tasks that air-traffic controllers have historically performed including maintaining separation between aircraft, sequencing and scheduling aircraft across locations in space, and avoiding airspace volumes like weather systems and restricted airspace.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: NASA E-Nose for the detection of disease using breath analysis
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Develop one or more AI models that can classify disease related biosignatures from the data produced by the Ames NASA E-Nose when exposed to exhaled human breath.
Purpose and benefits: Develop one or more AI models that can classify disease related biosignatures from the data produced by the Ames NASA E-Nose when exposed to exhaled human breath.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SERVIR Applied Deep Learning Book
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: The focus of the Applied Deep Learning Book is to provide practitioners with a wide variety of applied examples of Remote Sensing Deep Learning approaches. With each chapter focusing on a specific problem set such as object detection of downscaling using Deep Learning. Additionally, throughout the books chapters various examples are provided spanning the aforementioned  thematic areas.
Purpose and benefits: Thereby providing a wide variety of thematic applications to complement reader’s domain specific practical knowledge such as agronomy or forestry etc. We suspect readers are coming to this virtual book with preexisting geospatial expertise. However, limited Deep Learning knowledge and application specifically around environmental and Remote Sensing oriented challenges.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Atomistic Force Field Accelerator (ARFFA)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Training of machine learning models of physically informed neural network interatomic force fields for molecular dynamics simulations of material systems of interest at the atomic level
Purpose and benefits: Training of machine learning models of physically informed neural network interatomic force fields for molecular dynamics simulations of material systems of interest at the atomic level
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Advanced Air Mobility Digital Assistant
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A digital assistant using open source large language models designed to assist Systems Engineerins with navigating the complex web of requirements surrounding Urban Air Mobility. The system also utilizes a RAG approach to recommend relevant FAA requirements, regulations, and conops to the user.
Purpose and benefits: A digital assistant using open source large language models designed to assist Systems Engineerins with navigating the complex web of requirements surrounding Urban Air Mobility. The system also utilizes a RAG approach to recommend relevant FAA requirements, regulations, and conops to the user.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Large language models for food security outlooks
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: USAID Famine Early Warning Network (FEWSNET) produces food security outlooks and bulletins that can complement the perspective of Earth observations. We are exploring how large language models can facilitate the interpretation of these bulletins and outlooks for an integrated approach of food security informed from the perspectives of ground-level reports and from satellites.
Purpose and benefits: USAID Famine Early Warning Network (FEWSNET) produces food security outlooks and bulletins that can complement the perspective of Earth observations. We are exploring how large language models can facilitate the interpretation of these bulletins and outlooks for an integrated approach of food security informed from the perspectives of ground-level reports and from satellites.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Foundation model for Weather and Climate
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Custom network design that uses MERRA data to learn the atmospheric processes. The Foundation Model can then be adapted to for many different applications including downscaling and parameterization
Purpose and benefits: Custom network design that uses MERRA data to learn the atmospheric processes. The Foundation Model can then be adapted to for many different applications including downscaling and parameterization
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Foundation Model for Helio Physics
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: AI Foundation for Heliophysics to enable the research community build AI application on the SDO data sets
Purpose and benefits: AI Foundation for Heliophysics to enable the research community build AI application on the SDO data sets
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Design Optimization of Turbomachinery Rotor Blades using Neural Network Surrogate Models
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: A sample is made of a design space using Latin hypercube sampling. The geometry for these samples is then generated and evaluated using simulation tools. The result is then used to train a neural network for use as a surrogate model in design optimization.
Purpose and benefits: Early work shows R^2 > 0.99 (compared to FEA results) on both test and validation datasets when predicting structural performance of a rotor blade as a function of 6 design variables
Topic area: Science
Development stage: Pilot
First used: 2025-05-01
US federal AI use case: Spaceport Throughput Analysis Resource
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The Spaceport Throughput Analysis Resource (STAR) assesses whether a set proposed launches and associated activities can be performed given the resources KSC currently has available, and external constraints imposed on KSC operations.
Purpose and benefits: The Spaceport Throughput Analysis Resource (STAR) assesses whether a set proposed launches and associated activities can be performed given the resources KSC currently has available, and external constraints imposed on KSC operations.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Deep Learning Inhale Breath Detection Model to Support CO2 Washout Suit TestingInspired CO2 Algorithm for Respiration User Signals (ICARUS): A Deep Learning Model to Enable Real-Time and Post-Test ppCO2 Inspired Calculations
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: A Deep Learning Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) model is being developed for determining the start and end of inhale breaths for CO2 washout suit testing.
Purpose and benefits: A Deep Learning Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) model is being developed for determining the start and end of inhale breaths for CO2 washout suit testing.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SVM Classification of Write-in Topics from MSD Customer Sat Survey
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Support Vector Machine used to analyze write-in feedback from annual NASA MSD Customer Sat Survey, to classify comments into roughly 30 topics for visualization by Voice of the Customer Explorer (VOCE) dashboard
Purpose and benefits: AIML lets us surface insights from MSD Customer Sat Survey write-in comments that otherwise would be ignored.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Solar Sail Shape Reconstruction
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: The intended use of this model is to predict the shape of a solar sail in flight.
Purpose and benefits: The intended use of this model is to predict the shape of a solar sail in flight.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ATLAS
Agency: National Aeronautics and Space Administration - GISS: Goddard Institute for Space Studies
Problem addressed: Atlas is a RAG-based solution for answering questions about NASA standards, directives, and Space Act Agreements. It uses publicly available NASA standards, directives and Space Ac Agreements. Users ask questions to Atlas which then fines relevant excerpts from the corpus of NASA public data in order to provide answers.
Purpose and benefits: The framework developed for this may be applied to other high interest data sets.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NASA-GPT: Retrieval-Augmented Generation using NTRS and more
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: This use case is an example of Retrieval-Augmented Generation (RAG), which roughly speaking is an AI-augmented form of search. It acts much like ChatGPT or one of the other services except that it also provides references.
Purpose and benefits: This use case is an example of Retrieval-Augmented Generation (RAG), which roughly speaking is an AI-augmented form of search. It acts much like ChatGPT or one of the other services except that it also provides references.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Hierarchical U.N. Standard Product and Service Code Classification through logistic regression with the scikit-learn library
Agency: National Aeronautics and Space Administration - HQ: Headquarters
Problem addressed: Aid in UNSPSC classification** of SEWP Contract Line Item Numbers (CLIN).
Purpose and benefits: Aid in UNSPSC classification** of SEWP Contract Line Item Numbers (CLIN).
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ChatGSFC
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: ChatGSFC is an AI-powered chatbot designed for Goddard Space Flight Center personnel Powered by a state-of-the-art large language model (currently Claude 3.5 Sonnet) Capable of assisting with a wide range of tasks: General questions and quick research Document editing Code explanation and generation Data analysis
Purpose and benefits: Increased productivity Quick access to information Assistance with complex problem-solving
Topic area: Science
Development stage: Pilot
First used: 2025-04-29
US federal AI use case: General AI LLM Chatbot for NASA CUI Data: Accelerated Langley Transformation Initiative Research Assistant (ALTIRA)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Accelerated Langley Transformation Initiative Research Assistant (ALTIRA) is an artificial intelligence (AI) chatbot planned for general-purpose use for processing NASA CUI (not ITAR) data.
Purpose and benefits: Accelerated Langley Transformation Initiative Research Assistant (ALTIRA) is an artificial intelligence (AI) chatbot planned for general-purpose use for processing NASA CUI (not ITAR) data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Aerial AId: Visual data processing with AI in emergency scenarios for quick decision making
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: The Aerial AId activity enables the use of perception engines – primarily classification and object detection algorithms – in small uncrewed aerial systems (sUAS) for sUAS medical emergency response service providers. The goal of Aerial AId is to demonstrate a prototype optimized and trained perception engine, application specific datasets, and assurance framework.
Purpose and benefits: This work will specifically enable stakeholders in the commercial sUAS sector that are well positioned to develop sUAS concepts for emergency medical response applications.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Implement machine learning in NMIS to assist with mishap coding
Agency: National Aeronautics and Space Administration - OSMA: Office of the Chief Safety & Mission Assurance
Problem addressed: Leverage Machine Learning to train computers to review mishap and close call data in NMIS to suggest event coding characteristics to help mishap program managers classify events more efficiently.
Purpose and benefits: Reduce manual work, increase time and efficiency, and free up skilled subject matter experts for more complex tasks.
Topic area: Science
Development stage: Pilot
First used: 2026-03-30
US federal AI use case: XR Enabling Technologies for Hardware Integration & Test Process Improvement
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project will integrate speech-to-text and large language model functionality into the Mixed Reality Engineering Toolkit (MRET) to enable hands-free control and a natural-language interface for the program.
Purpose and benefits: XR Enabling Technologies for Hardware Integration & Test Process Improvement
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: DSM Autonomy: Capability Integration/Demonstration and Mission Resilience
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: DSM Autonomy: Capability Integration/Demonstration and Mission Resilience
Purpose and benefits: DSM Autonomy: Capability Integration/Demonstration and Mission Resilience
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Implementing Intelligent Extensible Mission Architectures (IEMA)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Implementing Intelligent Extensible Mission Architectures (IEMA)
Purpose and benefits: Implementing Intelligent Extensible Mission Architectures (IEMA)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A diagnostic package to facilitate and enhance chemical mechanism implementations within regional and global atmospheric chemistry models
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A diagnostic package to facilitate and enhance chemical mechanism implementations within regional and global atmospheric chemistry models
Purpose and benefits: A diagnostic package to facilitate and enhance chemical mechanism implementations within regional and global atmospheric chemistry models
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A machine learning tool to assist the validation of HPLC analysis of phytoplankton pigments at NASA GSFC
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A machine learning tool to assist the validation of HPLC analysis of phytoplankton pigments at NASA GSFC
Purpose and benefits: A machine learning tool to assist the validation of HPLC analysis of phytoplankton pigments at NASA GSFC
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A Neural Network Retrieval for Aerosol Optical Depth
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A Neural Network Retrieval for Aerosol Optical Depth
Purpose and benefits: A Neural Network Retrieval for Aerosol Optical Depth
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Adding Blowing Snow Diagnosis to the GEOS Capability for Improved Surface Mass Balance and Weather Applications
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Adding Blowing Snow Diagnosis to the GEOS Capability for Improved Surface Mass Balance and Weather Applications
Purpose and benefits: Adding Blowing Snow Diagnosis to the GEOS Capability for Improved Surface Mass Balance and Weather Applications
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data
Purpose and benefits: Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI Foundation Model for Weather and Climate
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: AI Foundation Model for Weather and Climate
Purpose and benefits: AI Foundation Model for Weather and Climate
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI Prediction Algorithms for Autonomous Science and Operations
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: AI Prediction Algorithms for Autonomous Science and Operations
Purpose and benefits: AI Prediction Algorithms for Autonomous Science and Operations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Algorithm developments for synergistic use of water quality products from multispectral and hyperspectral satellite observations
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Algorithm developments for synergistic use of water quality products from multispectral and hyperspectral satellite observations
Purpose and benefits: Algorithm developments for synergistic use of water quality products from multispectral and hyperspectral satellite observations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: An Urban Information System to Assess Neighborhood Climate Risk and Daily Exposures in Cities
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Developing ML models (e.g., random forests approaches) to capture climate responses in urban settings within NYC
Purpose and benefits: Shows how NASA tools can inform risk management in cities, with direct interest and engagement from local governments, community groups, and private sector partners. Many of these tools are in development (low TRL/ARL) but show great promise for wider improvements and applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Analysis of GES DISC datasets through NLP and TF-IDF techniques analyzing publications
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Analysis of GES DISC datasets through NLP and TF-IDF techniques analyzing publications
Purpose and benefits: Analysis of GES DISC datasets through NLP and TF-IDF techniques analyzing publications
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Applying machine learning in improving parameters in the global river routing model
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Applying machine learning in improving parameters in the global river routing model
Purpose and benefits: Applying machine learning in improving parameters in the global river routing model
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Applying Machine Learning to Earth Ozone Retrieval: Improving Scattering Calculations and Correcting Systematic Errors
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Applying Machine Learning to Earth Ozone Retrieval: Improving Scattering Calculations and Correcting Systematic Errors
Purpose and benefits: Applying Machine Learning to Earth Ozone Retrieval: Improving Scattering Calculations and Correcting Systematic Errors
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Automated Quality Control Scheme for GPM Satellite Precipitation Products
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Automated Quality Control Scheme for GPM Satellite Precipitation Products
Purpose and benefits: Automated Quality Control Scheme for GPM Satellite Precipitation Products
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Automated segmentation of chemical and mineralogical maps
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Automated segmentation of chemical and mineralogical maps
Purpose and benefits: Automated segmentation of chemical and mineralogical maps
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomous Network Security
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Autonomous Network Security
Purpose and benefits: Autonomous Network Security
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Autonomous Obstacle Avoidance of Mobile Robots using Reinforcement Learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Autonomous Obstacle Avoidance of Mobile Robots using Reinforcement Learning
Purpose and benefits: Autonomous Obstacle Avoidance of Mobile Robots using Reinforcement Learning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomus Path Planning of Robot Manipulators using Reinforcement Learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Autonomus Path Planning of Robot Manipulators using Reinforcement Learning
Purpose and benefits: Autonomus Path Planning of Robot Manipulators using Reinforcement Learning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Background matrix Generation with Stable Diffusion
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Background matrix Generation with Stable Diffusion
Purpose and benefits: Background matrix Generation with Stable Diffusion
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Bias correction of fluorescence retrievals with neural networks
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Bias correction of fluorescence retrievals with neural networks
Purpose and benefits: Bias correction of fluorescence retrievals with neural networks
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Bias correction of GEOS S2S forecast using machine learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Bias correction of GEOS S2S forecast using machine learning
Purpose and benefits: Bias correction of GEOS S2S forecast using machine learning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Bias correction to GOES-R PM2.5 for US EPA AirNow system
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Bias correction to GOES-R PM2.5 for US EPA AirNow system
Purpose and benefits: Bias correction to GOES-R PM2.5 for US EPA AirNow system
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Bidirectional water leaving signal extraction and correction using neural networks
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Bidirectional water leaving signal extraction and correction using neural networks
Purpose and benefits: Bidirectional water leaving signal extraction and correction using neural networks
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Boosting Inversion Research and Data Processing at GSFC with AI
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Boosting Inversion Research and Data Processing at GSFC with AI
Purpose and benefits: Boosting Inversion Research and Data Processing at GSFC with AI
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Building On Fundamentals:  Reducing Power and Improving Performance With Intelligent Radiometers
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Building On Fundamentals:  Reducing Power and Improving Performance With Intelligent Radiometers
Purpose and benefits: Building On Fundamentals:  Reducing Power and Improving Performance With Intelligent Radiometers
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: CALET Cosmic Ray Data Analysis
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: CALET Cosmic Ray Data Analysis
Purpose and benefits: CALET Cosmic Ray Data Analysis
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Code Assistant Pilot Study
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Link prediction
Purpose and benefits: Dataset Recommender
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Dataset Recommender
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Link prediction
Purpose and benefits: Deep learning for Environmental and Ecological Prediction-eValuation and Insight with Ensembles of Water quality (DEEP-VIEW)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Deep learning for Environmental and Ecological Prediction-eValuation and Insight with Ensembles of Water quality (DEEP-VIEW)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Modular framework for multi-sensor segmentation
Purpose and benefits: Demonstrate High-Level Space Mission Requirement Flowdown to Software Requirements Using Generative Artificial Intelligence (AI)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Demonstrate High-Level Space Mission Requirement Flowdown to Software Requirements Using Generative Artificial Intelligence (AI)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Automated identification of cloud ice crystal types and supercooled liquid
Purpose and benefits: Determining Cloud Particle Types Using Backscatter Lidar Data and a Clustering Approach
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Determining Cloud Particle Types Using Backscatter Lidar Data and a Clustering Approach
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Automated identification of cloud ice crystal types and supercooled liquid
Purpose and benefits: Significant time savings
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of a machine learning emulator for CAM-CMAQ to improve wildfire smoke forecasts
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Development of a machine learning emulator for CAM-CMAQ to improve wildfire smoke forecasts
Purpose and benefits: Development of a machine learning emulator for CAM-CMAQ to improve wildfire smoke forecasts
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of digital twin technologies for climate prediction
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Development of digital twin technologies for climate prediction
Purpose and benefits: Development of digital twin technologies for climate prediction
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of real-time high-resolution air quality maps through combination of model output, satellite data, and ancillary Google datasets
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Development of real-time high-resolution air quality maps through combination of model output, satellite data, and ancillary Google datasets
Purpose and benefits: Development of real-time high-resolution air quality maps through combination of model output, satellite data, and ancillary Google datasets
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Dynamic Time Interpolation using Spherical Harmonic Neural Operator Models
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Dynamic Time Interpolation using Spherical Harmonic Neural Operator Models
Purpose and benefits: Dynamic Time Interpolation using Spherical Harmonic Neural Operator Models
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ECCOH & "Quick Chemistry" for Atmospheric Composition and Climate Studies
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: ECCOH & "Quick Chemistry" for Atmospheric Composition and Climate Studies
Purpose and benefits: ECCOH & "Quick Chemistry" for Atmospheric Composition and Climate Studies
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ESDIS VisML
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: ESDIS VisML
Purpose and benefits: ESDIS VisML
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Estimate global satellite AOD from visible to ultra-violet
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Estimate global satellite AOD from visible to ultra-violet
Purpose and benefits: Estimate global satellite AOD from visible to ultra-violet
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Fast neural network emulator for vector radiative transfer simulations
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Fast neural network emulator for vector radiative transfer simulations
Purpose and benefits: Fast neural network emulator for vector radiative transfer simulations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Fast radiative transfer emulation for GHG retrievals
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Fast radiative transfer emulation for GHG retrievals
Purpose and benefits: Fast radiative transfer emulation for GHG retrievals
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Fast Retrieval Emulators for Instrumenting Climate Models
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Fast Retrieval Emulators for Instrumenting Climate Models
Purpose and benefits: Fast Retrieval Emulators for Instrumenting Climate Models
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: FluxSat Gross Primary Production - global upscaling of eddy covariance with machine learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: FluxSat Gross Primary Production - global upscaling of eddy covariance with machine learning
Purpose and benefits: FluxSat Gross Primary Production - global upscaling of eddy covariance with machine learning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: GAIT: Generative AI Teleoperations for Robotics
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: GAIT: Generative AI Teleoperations for Robotics
Purpose and benefits: GAIT: Generative AI Teleoperations for Robotics
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Generalized photometric neural network framework
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Generalized photometric neural network framework
Purpose and benefits: Generalized photometric neural network framework
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Generalized time-series anomaly detection for rapid data survey of in-situ observations
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Generalized time-series anomaly detection for rapid data survey of in-situ observations
Purpose and benefits: Generalized time-series anomaly detection for rapid data survey of in-situ observations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Generating localized air quality forecasts for US embassies using ML and GEOS-FP
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Generating localized air quality forecasts for US embassies using ML and GEOS-FP
Purpose and benefits: Generating localized air quality forecasts for US embassies using ML and GEOS-FP
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Giovanni GPT
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Giovanni GPT
Purpose and benefits: Giovanni GPT
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Ground Safety AI
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Ground Safety AI
Purpose and benefits: Ground Safety AI
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Hybrid Model to emulate GEOS physics
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Hybrid Model to emulate GEOS physics
Purpose and benefits: Hybrid Model to emulate GEOS physics
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Identification of dust from MODIS/VIIRS sensors using ML
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Identification of dust from MODIS/VIIRS sensors using ML
Purpose and benefits: Identification of dust from MODIS/VIIRS sensors using ML
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improving greenhouse gas retrievals using a PCA-NN technique
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Improving greenhouse gas retrievals using a PCA-NN technique
Purpose and benefits: Improving greenhouse gas retrievals using a PCA-NN technique
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improving quality of SO2 retrievals using a machine learning based analysis technique
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Improving quality of SO2 retrievals using a machine learning based analysis technique
Purpose and benefits: Improving quality of SO2 retrievals using a machine learning based analysis technique
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improving uncertainty characterization for PACE polarimeters
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Improving uncertainty characterization for PACE polarimeters
Purpose and benefits: Improving uncertainty characterization for PACE polarimeters
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Integrating Research in Artificial Intelligence for Spacecraft Resilience (RAISR) into a FlatSat Hardware System
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Integrating Research in Artificial Intelligence for Spacecraft Resilience (RAISR) into a FlatSat Hardware System
Purpose and benefits: Integrating Research in Artificial Intelligence for Spacecraft Resilience (RAISR) into a FlatSat Hardware System
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Investigating the vanishing martian induced magnetosphere boundary using a Machine Learning approach
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Investigating the vanishing martian induced magnetosphere boundary using a Machine Learning approach
Purpose and benefits: Investigating the vanishing martian induced magnetosphere boundary using a Machine Learning approach
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Localized air quality forecasts for cities in Africa and South America
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Localized air quality forecasts for cities in Africa and South America
Purpose and benefits: Localized air quality forecasts for cities in Africa and South America
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Space Mission Applications
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Machine Learning for Space Mission Applications
Purpose and benefits: Machine Learning for Space Mission Applications
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Applying machine learning techniques to enhance the sensitivity and selectivity of multifunctional sensor platform
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work applies machine learning techniques to enhance the sensitivity and selectivity of the multifunctional sensor platform, a very small circuit board which includes multiple tiny sensors producing a variety of data.
Purpose and benefits: Applying machine learning techniques to enhance the sensitivity and selectivity of multifunctional sensor platform
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning volcanic SO2 height retrievals
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Machine Learning volcanic SO2 height retrievals
Purpose and benefits: Machine Learning volcanic SO2 height retrievals
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine-enabled modeling of terminus ablation for Greenland's outlet glaciers
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Machine-enabled modeling of terminus ablation for Greenland's outlet glaciers
Purpose and benefits: Machine-enabled modeling of terminus ablation for Greenland's outlet glaciers
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine-Learning based Auroral Ionospheric electrodynamics Model (ML-AIM)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A suite of machine learning models for high-latitude electrodynamics
Purpose and benefits: Machine-Learning based Auroral Ionospheric electrodynamics Model (ML-AIM)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine-learning based radiative transfer model and LWP/IWP
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Machine-learning based radiative transfer model and LWP/IWP
Purpose and benefits: Machine-learning based radiative transfer model and LWP/IWP
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MAGSTAR: Magnetometer Interference Mitigation with Statistical Decomposition and Artificial Intelligence
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: MAGSTAR: Magnetometer Interference Mitigation with Statistical Decomposition and Artificial Intelligence
Purpose and benefits: MAGSTAR: Magnetometer Interference Mitigation with Statistical Decomposition and Artificial Intelligence
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Making a long-term data record of opportunistic experiments with deep learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Making a long-term data record of opportunistic experiments with deep learning
Purpose and benefits: Making a long-term data record of opportunistic experiments with deep learning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MATISSE: MAchine inTelligence for Small SatellitEs - application of AI to permit autonomic science triage for small, power starved satellites
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: MATISSE: MAchine inTelligence for Small SatellitEs - application of AI to permit autonomic science triage for small, power starved satellites
Purpose and benefits: MATISSE: MAchine inTelligence for Small SatellitEs - application of AI to permit autonomic science triage for small, power starved satellites
Topic area: Science
Development stage: Pre-deployment