GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Estimation of Multilayered Cloud Properties from Low-Earth-Orbit Satellite Imager Data
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: An artificial neural network is developed and applied to satellite imagery data to identify and retrieve characteristics of multi-layered clouds for potential use in weather and climate applications.
Purpose and benefits: An artificial neural network is developed and applied to satellite imagery data to identify and retrieve characteristics of multi-layered clouds for potential use in weather and climate applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Geostationary Satellite Sounder Pathfinder Project for 4-D Atmospheric Thermodynamics and Winds
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A k-nearest neighbors technique, coupled with an advanced data assimilation system are applied to hyperspectral infrared and geostationary satellite imager data to provide high spatiotemporal resolution analyses of atmospheric thermodynamics, winds and clouds for severe weather forecasting, cloud process studies and other meteorological applications.
Purpose and benefits: A k-nearest neighbors technique, coupled with an advanced data assimilation system are applied to hyperspectral infrared and geostationary satellite imagery data to provide high spatiotemporal resolution analyses of atmospheric thermodynamics, winds and clouds for severe weather forecasting, cloud process studies and other meteorological applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Applies convolutional neural networks (supervised learning) to automatically identify the presence of different aerosol species (e.g., dust and smoke) in CALIPSO lidar backscatter measurements.
Purpose and benefits: Applies convolutional neural networks (supervised learning) to automatically identify the presence of different aerosol species (e.g., dust and smoke) in CALIPSO lidar backscatter measurements.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Identifying flaws in manufacturing of composites
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Inspection for manufacturing flaws makes up 40% of fabrication time so if that time could be reduced it would have a significant impact on overall manufacturing time.  That doesn’t even take into account the benefits of finding flaws that were previously undetectable by the current technique of visual inspection.
Purpose and benefits: Inspection for manufacturing flaws makes up 40% of fabrication time so if that time could be reduced it would have a significant impact on overall manufacturing time.  That doesn’t even take into account the benefits of finding flaws that were previously undetectable by the current technique of visual inspection.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improvements in Global Nighttime Satellite Cloud Analyses for Weather and Climate
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A k-nearest neighbors method is applied to overcome nighttime infrared satellite imagery sensitivity limits that dramatically improves nighttime cloud analyses and their consistency with daytime analyses.
Purpose and benefits: A k-nearest neighbors method is applied to overcome nighttime infrared satellite imagery sensitivity limits that dramatically improves nighttime cloud analyses and their consistency with daytime analyses.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improving CERES Low-Latency Surface Radiation Fluxes with Machine/Deep Learning
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: In conjunction with sophisticated radiative transfer simulations, the CERES (Clouds and the Earth's Radiant Energy System) team is using machine and deep learning methods to improve near real-time surface radiative fluxes for clean energy, infrastructure energy use, and agricultural applications.
Purpose and benefits: In conjunction with sophisticated radiative transfer simulations, the CERES (Clouds and the Earth's Radiant Energy System) team is using machine and deep learning methods to improve near real-time surface radiative fluxes for clean energy, infrastructure energy use, and agricultural applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Intelligent Contingency Management
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Adapt and train AI algorithms to contribute to an autonomous vehicle mission manager for Advanced Air Mobility (Cargo, Air Taxis). At a high level, the AI must recognize contingency flight conditions and react appropriately to return the aircraft to safe flight status. The project has three main objectives: 1. Explore machine learning for intelligent contingency management, with a focus on assessing/projecting vehicle capability and maintaining nominal performance via reinforcement learning. 2. Develop vehicle intelligent contingency management system architecture at a functional level and validate against a specific Unmanned Air Mobility (UAM)-class vehicle. 3. Incorporate (1) and (2) into an evolving toolset for an autonomous vehicle.
Purpose and benefits: Adapt and train AI algorithms to contribute to an autonomous vehicle mission manager for Advanced Air Mobility (Cargo, Air Taxis). At a high level, the AI must recognize contingency flight conditions and react appropriately to return the aircraft to safe flight status. The project has three main objectives: 1. Explore machine learning for intelligent contingency management, with a focus on assessing/projecting vehicle capability and maintaining nominal performance via reinforcement learning. 2. Develop vehicle intelligent contingency management system architecture at a functional level and validate against a specific Unmanned Air Mobility (UAM)-class vehicle. 3. Incorporate (1) and (2) into an evolving toolset for an autonomous vehicle.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Lessons Learned Bot (LLB)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: In near real-time, the Lessons Learned Bot, or LLB, brings lessons learned (LL) documents to users through a Microsoft Excel add-in application locally installed to search for LL content relevant to the text within the selected Excel cell. The application will encompass a corpus of documents, a trained Machine Learning (ML) model, built-in ML tools to train user’s documents, and an easy-to-use user interface to allow for the streamlined discovery of LL content.
Purpose and benefits: Today, NASA’s LL are online and searchable via keywords. Nevertheless, users often face a challenge to find lessons relevant to their issues. Applying the advancement in Natural Language Processing (NLP) ML algorithm, the LLB can find and rank LL records relevant to text in the user’s selected Excel cells, containing just a few words or entire paragraphs of text. Results are displayed to the user in their existing Excel workflow.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Advancing Risk Precursor Identification Tools in Commercial Airline Terminal Area Operations (out of D318)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: This work uses a variety of machine learning techniques to recommend commercial terminal area aviation failure modes in addition to unstable vertical approach, evaluate these for machine learning feasibility, recommend the best failure mode and machine learning algorithm pairings, and incorporate these into the aviation risk precursor detection prototype back-end and user interface.
Purpose and benefits: This work uses a variety of machine learning techniques to recommend commercial terminal area aviation failure modes in addition to unstable vertical approach, evaluate these for machine learning feasibility, recommend the best failure mode and machine learning algorithm pairings, and incorporate these into the aviation risk precursor detection prototype back-end and user interface.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Unsupervised anomaly detection in flight data with deep variational autoencoders
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: This model is an unsupervised deep learning based anomaly detection for aircraft flight data based on variational autoencoders with convolutional architecture. The model is designed to find anomalies in multivaraite time-series and can work with heterogeneous data.
Purpose and benefits: It is currently tested and validated in finding anomaly detection in flight's operational quality assurance data from commerical aircraft.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Mitigating Problematic GOES-17 Infrared Radiances at Night for Weather and Climate Applications
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A k-nearest neighbors method is applied to successfully reconstruct bad infrared radiance measurements at night caused by an instrument cooling problem on the GOES-17 (Geostationary Operational Environmental Satellite) Advanced Baseline Imager.
Purpose and benefits: A k-nearest neighbors method is applied to successfully reconstruct bad infrared radiance measurements at night caused by an instrument cooling problem on the GOES-17 (Geostationary Operational Environmental Satellite) Advanced Baseline Imager.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NASA OCIO STI Concept Tagging Service
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: An API (application program interface) for exposing topic models created with the STI (Scientific & Technical Information) concept training repository.
Purpose and benefits: An API (application program interface) for exposing topic models created with the STI (Scientific & Technical Information) concept training repository.
Topic area: Science
Development stage: Deployed
US federal AI use case: New Skin Temperature Analyses for Satllite Cloud Retrievals
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A deep neural network is applied to model analyses and satellite observations to improve skin temperature estimates needed for satellite remote sensing of cloud properties.
Purpose and benefits: A deep neural network is applied to model analyses and satellite observations to improve skin temperature estimates needed for satellite remote sensing of cloud properties.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Nocturnal Opaque Ice Cloud Optical Depth Analyses from MODIS Multispectral Infrared Radiances
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: An artificial neural network is developed and applied to improve estimates of opaque ice cloud optical depths at night using MODIS (Moderate Resolution Imaging Spectroradiometer) data.
Purpose and benefits: An artificial neural network is developed and applied to improve estimates of opaque ice cloud optical depths at night using MODIS (Moderate Resolution Imaging Spectroradiometer) data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PACE-MAPP
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: The MAPP (modeling, analysis and prediction program) remote sensing retrieval algorithm for the upcoming PACE (plankton, aerosol, cloud, ocean ecosystem) satellite mission uses neural networks to drastically improve the computational speed and capability of determining aerosol, cloud, and ocean properties from space-based measurements of the Earth.
Purpose and benefits: This framework can be used to train neural networks for a variety of satellite and airborne sensors, including passive polarimeter and hyperspectral sensors and active lidar instruments, to better understand the Earth's dynamic atmosphere, ocean and land systems.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Polar Nighttime Cloud Detection Using an Artificial Neural Network
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: An artificial neural network is applied that addresses the difficult problem of accurately detecting clouds at night over snow- and ice-covered areas in polar regions from satellite imager data.
Purpose and benefits: An artificial neural network is applied that addresses the difficult problem of accurately detecting clouds at night over snow- and ice-covered areas in polar regions from satellite imager data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Probabilistic calibration framework for finite element thermal process modeling of metallic additive manufacturing. Application to promote certification/qualification of load critical aerospace flight parts.
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Application of active learning paradigm to efficiently develop Gaussian Process Regression surrogate where run time for the target finite element thermal process model is significant.  Fully trained surrogate is then used by monte carlo process to develop probabilistic distribution of finite element model calibration variables which result in finite element model predictions that are in line with input empirical measurement distributions.
Purpose and benefits: In short, this technique enables the robust and efficient calibration of a model simulating the manufacture of metallic parts by additive manufacturing.  This is important because pure simulation of these processes is not possible without calibration due to large uncertainties in fundamental physical and material properties currently available due to the nature of the manufacturing process.  The inherent variation in mechanical properties of parts produced using metallic additive manufacturing result in significant challenges to certification/qualification for flight.  The goal is to alleviate these challenges with better probabilistic quantification of the fundamentals resulting in this inherent variation, thus promoting further adoption of this fledgling manufacturing technique by industry within the aeronautics industry.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: This study uses ocean surface wind speed derived from microwave radiometer to train spacebased lidar measurements to measure both wind speed and aerosol optical depth using neural networks.
Purpose and benefits: This study uses ocean surface wind speed derived from microwave radiometer to train spacebased lidar measurements to measure both wind speed and aerosol optical depth using neural networks.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Severe Storm Prediction via Overshooting Cloud Top and Above Anvil Cirrus Plume Image Recognition from Satellite Imager Data
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Overshooting Cloud Tops (OTs) Above-Anvil Cirrus Plumes (AACPs) are indicators of especially intense thunderstorm updrafts that are precursors to severe weather including damaging winds, hail, tornadoes, lightning, flooding rainfall, and aviation weather hazards. This project involves training machine learning image recognition techniques to identify OTs AACPs in multispectral satellite imagery (visible, infrared, and lightning imaging) for storm warning.  Such warnings are especially valuable in regions without near real time weather radar networks or during radar outages. Applying these methods to long-term satellite data records enables the community to assess severe storm risk, which is needed by the insurance and reinsurance industries
Purpose and benefits: A NASA open source software tool has been made publicly available to enable researchers and forecasters to apply the tool in near real time and for archived satellite imagery.  This is the first tool of its kind that can be used for satellite-based severe storm detection https://github.com/nasa/svrstormsig
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Aero-Engines AI - a machine-learning app for aircraft engine system-performance prediction
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: Aero-Engines AI is a Windows app that deploys machine-learning analytics to predict aircraft engine performance.
Purpose and benefits: Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using Natural Language Processing to Help Automate the Standardization of PI Variable Names from ICARTT Files
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Variables names of data collected from suborbital missions need mapping to the corresponding standard variable names for consistency in labelling the available science data, enhancing data discovery for further research; ASDC (Atmospheric Science Data Center) is developing an AI/ML based workflow for the assignment of these standard variable names based on Natural Language Processing (NLP).
Purpose and benefits: Variables names of data collected from suborbital missions need mapping to the corresponding standard variable names for consistency in labelling the available science data, enhancing data discovery for further research; ASDC (Atmospheric Science Data Center) is developing an AI/ML based workflow for the assignment of these standard variable names based on Natural Language Processing (NLP).
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Airplane detection
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Deep learning-based airplane detection from high-resolution satellite imagery
Purpose and benefits: airplane detection from high-resolution satellite imagery
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Automated Dust detection in satellite imagery
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Application of machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery to identify dust in satellite imagery and output the probability dust is present
Purpose and benefits: Application of machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery to identify dust in satellite imagery and output the probability dust is present
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Deep Learning-based Hurricane Intensity Estimator
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: A web-based situational awareness tool that uses deep learning on satellite images to objectively estimate windspeed of a hurricane
Purpose and benefits: A web-based situational awareness tool that uses deep learning on satellite images to objectively estimate windspeed of a hurricane
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: GCMD Keyword Recommender (GKR)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Natural Language Processing-based science keyword suggestion tool
Purpose and benefits: Natural Language Processing-based science keyword suggestion tool
Topic area: Science
Development stage: Deployed
First used: 2023-01-01
US federal AI use case: Marine debris detection using deep learning and high resolution satellite images
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Floating marine debris is a global pollution problem which threatens marine and human life and leads to the loss of biodiversity. Large swaths of marine debris are also navigational hazards to vessels. This project uses deep learning to detect floating marine debris in satellite imagery.
Purpose and benefits: Floating marine debris is a global pollution problem which threatens marine and human life and leads to the loss of biodiversity. Large swaths of marine debris are also navigational hazards to vessels. This project uses deep learning to detect floating marine debris in satellite imagery.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Near-Reality Slosh Model using AIML
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Liquid slosh for aircraft and spacecraft is a highly challenging topic; traditional slosh modeling & simulation approaches are very computationally expensive. This project seeks to use AIML techniques such as neural networks to create ML surrogate models to accurately approximate traditional techniques.
Purpose and benefits: Time saving for slosh model generation
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Phenomena Detection Portal
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: A system that detects Earth science phenomena from the image archives using deep learning to provide efficient search and discovery of Earth science events
Purpose and benefits: efficient search and discovery of Earth science events
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Pixel-Level Smoke Detection Model with Deep Learning
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Automated, deep learning based detection model capable of identifying smoke plumes from shortwave reflectance for the Geostationary Operational Environmental Satellite R series of satellites.
Purpose and benefits: Automated, deep learning based detection model capable of identifying smoke plumes from shortwave reflectance for the Geostationary Operational Environmental Satellite R series of satellites.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Predicting streamflow with deep learning
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Uses a long short-term memory model to predict streamflow at USGS gauges sites
Purpose and benefits: Uses a long short-term memory model to predict streamflow at USGS gauges sites
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Ship detection
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Deep learning-based ship detection from high-resolution satellite imagery
Purpose and benefits: Deep learning-based ship detection from high-resolution satellite imagery
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Sinatra Software for Anomaly Detection
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Flexible software framework that analyzes various input sources of data and detects anomalies​. Automates neural network model creation and tuning​.
Purpose and benefits: Makes it easier for ML beginners to get started.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: INtelligent StennIs Gas House Technology (INSIGHT)
Agency: National Aeronautics and Space Administration - SSC: Stennis Space Center
Problem addressed: INSIGHT is an operational system that performs autonomous Integrated System Health Management (ISHM) and autonomous operations of the Nitrogen System of the High Pressure Gas Facility at NASA Stennis Space Center.
Purpose and benefits: It is an application implemented using the NASA Platform for Autonomous Systems (NPAS) described in this document as AI Use Case Name: NASA Platform for Autonomous Systems (NPAS).
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NASA Platform for Autonomous Systems (NPAS)
Agency: National Aeronautics and Space Administration - SSC: Stennis Space Center
Problem addressed: The NASA Platform for Autonomous Systems (NPAS) enables implementation of "thinking" systems, and in particular of "thinking" autonomous systems. A broad range of systems can be made to display "thinking" autonomous behavior; including fluid, mechanical, electrical, networks, and computer. Additional types of systems can be easily included.
Purpose and benefits: NPAS systems/applications incorporate autonomy strategies to deal with off-nominal cases, and strategies for fault management. NPAS includes infrastructure for autonomous operations (task definition, planning, scheduling, and execution) embodying specific concepts of operations. NPAS supports implementation of hierarchical distributed autonomous systems and operations.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomous WAiting Room Evaluation (AWARE)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Using an existing security camera and YOLO Machine Learning model to detect and count number of people waiting for service at Langley's Badge & Pass Office. When a predetermined threshold of people is exceeded, automated texts and emails are sent to request additional help at the service counters.
Purpose and benefits: Spreads load on LaRC Badge & Pass Office (via website telling employees how busy) and adds BPO support by texting staff when waiting room conut threshold exceeded.
Topic area: Administrative functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This AI processes images of people waiting in the Langley Badge & Pass office. The people are counted and their images immediately deleted to avoid privacy issues.
First used: 2023-01-01
US federal AI use case: Graph Neural Networks for Airfoil Performance Prediction
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: We are investigating the use of Graph Convolutional Neural Networks to learn relationship between airfoil coordinates and predict the performance for aerodynamics analysis. Inputs include the shape of the airfoil and outputs are the coefficient of lift, drag, and moment.
Purpose and benefits: The impact is we have a new type of neural network architecture that we can potentially use for other projects.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Pre-trained microscopy image neural network encoders
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: Convolutional Neural Network encoders were trained on over 100,000 microscopy images of materials.
Purpose and benefits: When deployed in downstream microscopy tasks through transfer learning, encoders pre-trained on MicroNet outperform ImageNet encoders. These pre-trained MicroNet encoders have been successfully deployed for semantic segmentation, instance segmentation, and regression tasks.
Topic area: Science
Development stage: Deployed
First used: 2022-09-19
US federal AI use case: Surrogate Models for Efficient Multiscale Modeling of Composite Materials
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: A custom neural network architecture containing graph convolutional network (GCN) and long short-term memory (LSTM) layers was trained as a computationally efficient surrogate for a physics-based composite modeling simulation.
Purpose and benefits: Surrogate models are attractive because they can be evaluated many orders of magnitude faster than physics-based models and with a high degree of accuracy. Such models can be used for efficient multiscale modeling, design optimization, Monte Carlo methods, and optimal experimental design in ways that would be intractable with many physics-based models.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Federated Learning Using In-Space Data (FLUID )
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Enables neural net models to be trained using a combination of terrestrial data and space-borne data without the need to downlink or uplink data for consolidation and training in the normal manner. Instead, small components of the overall neural net model are trained in-situ with the data and transmitted for federation into a single neural net, thereby reducing data transmission demands and reducing overall latencies by several orders of magnitude.
Purpose and benefits: Enables neural net models to be trained using a combination of terrestrial data and space-borne data without the need to downlink or uplink data for consolidation and training in the normal manner. Instead, small components of the overall neural net model are trained in-situ with the data and transmitted for federation into a single neural net, thereby reducing data transmission demands and reducing overall latencies by several orders of magnitude.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Mapping Digital Infrastructure (MADI)
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The Mapping Digital Infrastructure (MADI) is a series of natural-language processing based tools that are designed to supplement the human-in-the-loop data exploration for CAS.
Purpose and benefits: As CAS's Mapping and Synthesis teams work to identify transformational, wicked problems and potential solutions, MADI aims to supplement the information gathering and synthesis steps by clustering, theming, crawling, and summarizing publicly available data about emerging trends, needs, and capabilities.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Use of AI for UAV power train monitoring
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Use of PyTorch feedforward neural networks to monitor the health of electronic speed controllers and motors.
Purpose and benefits: Use of PyTorch feedforward neural networks to monitor the health of electronic speed controllers and motors.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Inverse Design of Materials
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: Discovering new materials is typically a mix of art and science, with timelines to create and robustly test a new material mix / manufacturing method ranging from ten to twenty years. This project seeks to enable rapid discovery, optimization, qualifaction and deployment of fit-for-purpose materials.
Purpose and benefits: The project is currently being utilized in an NESC investigation to improve SLS core stage weld quality. The technology will be used to select experiments for a fully autonomous robotic lab that is currently being procured to design better insulating materials for electrified aircraft. 
Bayesian optimization frameworks predicts the next best simulation to run to minimize a target. For the PMC example, for instance, the objective was to minimize weight while maintaining a minimum margin of safety.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Nighttime Combustion Detection from NASA’s Black Marble
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This use case focuses on monitoring nighttime combustion over land from NASA’s Black Marble product and has demonstrated the detection of fires and gas flaring mapping by jointly using both thermal and light emission properties of these event.
Purpose and benefits: The ML capability first demonstrates the generation of training samples using anomaly detection, tackling the challenge of training data generation in Earth Sciences. This is followed by classification of combustion and background across diverse geographic reasons and seasons and produces detections from a suite of baseline ML approaches including per-band outlier detections, fully-connected neural networks and Siamese Networks trained with triplet and contrastive loss. The detections are then jointly considered to create a high confidence ensemble detection layer.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network to provide synthetic data points.
Purpose and benefits: The implementation uses classification methods and hierarchical clustering to identify genes that are predictive of outcomes.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NIR spectroscopy-based analytical tool for immediate determination of chemical signature
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Goals: Develop AI/ML based tool that can identify materials composition and size distribution “on the fly”
Purpose and benefits: Importance: NIR is well-suited for soft matter research in space because- 
1. It doesn’t require any sample preparation
2. It doesn’t need gravity driven flow 
3. Form factor can be of the order of a matchbox
Challenges: There is no tool presently available that can perform spectral decomposition to achieve the goals
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improved Differential Dynamic Microscopy (DDM) tool for characterization of soft matter
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Goals: Develop AI/ML based tool that can improve processability of images and derive the outcome
Purpose and benefits: Importance: DDM is well-suited for soft matter research in space because- 
1. It only needs regular microscopy system
2. Can be extremely modular and can be fitted in form factor as small as cube sat
Challenges: The present methodology for DDM analysis require long time (~20- 30 minutes) for processing images &amp; decomposition of signal is a challenge
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: The On-board Artificial Intelligence Research (OnAIR) Platform
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The On-board Artificial Intelligence Research (OnAIR) Platform is a generalized software framework for performing AI research.
Purpose and benefits: OnAIR is intended to be used by Artificial Intelligence researchers that are exploring how to apply leading edge Artificial Intelligence research to flight software in a simulated environment. For example, a researcher may use OnAIR to develop a proof of concept demonstrating that a specialized technique can diagnose satellite faults in a simulated environment. OnAIR is not intended to be used in flight software.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Troupe Rover Demonstration
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The Troupe Rover demonstration project is implementing an algorithm called "SafeMAP" or Safe Multi-Agent Planner. SafeMAP is used in distributed multi-rover or multi-uav system to plan the actions of a group of collaborating agents working on a shared goal.
Purpose and benefits: The Troupe Rover demonstration project is implementing an algorithm called "SafeMAP" or Safe Multi-Agent Planner. SafeMAP is used in distributed multi-rover or multi-uav system to plan the actions of a group of collaborating agents working on a shared goal.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Manager for Intelligent Knowledge Access (MIKA)
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Manager for Intelligent Knowledge Access (MIKA) is a Python toolkit specialized for state-of-the-art knowledge discovery and information retrieval for technical documents.
Purpose and benefits: Intended for supporting early design hazard analysis, MIKA provides multiple natural language processing techniques and aims to enable rapid application of these different techniques to a relevant dataset.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AdaStress
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: AdaStress, a tool for finding and analyzing the likeliest failures in a simulated system under test.
Purpose and benefits: AdaStress, a tool for finding and analyzing the likeliest failures in a simulated system under test.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MADI - Strategic Foresight and Knowledge Management infrastructure for ARMD/TACP/CAS
Agency: National Aeronautics and Space Administration - GRC: Glenn Research Center
Problem addressed: The CAS Discovery team is developing, testing and using digital infrastructure to meet the pace of expected deliverables in an automated, efficient, collaborative manner, including taking advantage of AI/ML capabilities.
Purpose and benefits: A digital infrastructure would enable a curated problem database. It is essential to minimize rework due to not being able to find previous data, information, and analyses and having to repeat past work. This type of infrastructure would be adaptable to other innovation areas at NASA, especially those that are working in “problem space” rather than technology development space. In addition MADI is able to speculate on futures and generate scenarios. MADI is also able to provide recombinant innovation support to identify ways to use archived technologies and proposals in new ways.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Rover ML-based vision system
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The rover uses deep neural networks perform perception, currently related to line following.
Purpose and benefits: The rover uses deep neural networks perform perception, currently related to line following.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ImageLabeler
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Web-based Collaborative Machine Learning Training Data Generation Tool
Purpose and benefits: Web-based Collaborative Machine Learning Training Data Generation Tool
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Oceanic cold pool detection using scatterometer winds
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Use a U-Net convolutional neural network to detect oceanic cold pools in spaceborne scatterometer wind observations.
Purpose and benefits: Use a U-Net convolutional neural network to detect oceanic cold pools in spaceborne scatterometer wind observations.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Similarity Search for Earth Science Image Archive
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Self Supervised Based Learning approach to search image archives using a query image
Purpose and benefits: Self Supervised Based Learning approach to search image archives using a query image
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PASSION Computer
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: To develop a pipeline for processing raw overlappagram (other word?) images through the various data products. To produce the final data product in a timely manner.
Purpose and benefits: To develop a pipeline for processing raw overlappagram (other word?) images through the various data products. To produce the final data product in a timely manner.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Instantaneous Clarity of Ambient eNvironment Capability (ICAN-C)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: To determine the applicability of using AI/ML to correct obscured vision. Landing systems where sedement is blown around and/or dusty environments are the primary application.
Purpose and benefits: Improving safety in instances of obscurred vision
Topic area: Science
Development stage: Pilot
First used: 2025-08-01
US federal AI use case: Trajectory mission design
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: We use ML for a lot of different things. First, and most importantly, we use ML to estimate initial conditions for POST and Copernicus for mission design. We've also used ML for investigatory work trying to decipher what data is telling us and exploring datasets for root-cause analysis.
Purpose and benefits: We use ML for a lot of different things. First, and most importantly, we use ML to estimate initial conditions for POST and Copernicus for mission design. We've also used ML for investigatory work trying to decipher what data is telling us and exploring datasets for root-cause analysis.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: TAPAS (Track Augmentation and Performance Analysis System)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: TAPAS is a tool for automating the comparison of DSN telemetry data, designed to help DSN operators find potential anomalies faster and more efficiently.
Purpose and benefits: It has three autonomous functions: an automatic comparative analysis of a given track against a set of historical tracks, anomaly detection compared to a reference normal track, and statistical difference comparison between two given tracks. By using TAPAS, DSN operators can quickly identify discrepancies in the telemetry data and respond to them more effectively.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Onboard Planner for Mars2020 Rover (Perseverance)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: The M2020 onboard scheduler incrementally constructs a feasible schedule by iterating through activities in priority-first order.
Purpose and benefits: Research, experiments, and engineering to empower future rovers with onboard autonomy; planning, scheduling & execution; path planning; onboard science; image processing; terrain classification; fault diagnosis; and location estimation. This is a multi-faceted effort and includes experimentation and demonstrations on-site at JPL's simulated mars navigation yard.
Topic area: Science
Development stage: Deployed
First used: 2020-07-01
US federal AI use case: SensorWeb: Volcano, Flood, Wildfire, and others.
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: The Sensor Web Project uses a network of sensors linked by software and the internet to an autonomous satellite observation response capability.
Purpose and benefits: This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program to study volcanos. We have also run sensorweb tests to study flooding, cryosphere events, and atmospheric phenomena. Specifically, in our application, we use low resolution, high coverage sensors to trigger observations by high resolution instruments. Note that there are many other rationales to network sensors into a sensorweb. For example automated response might enable observation using complementary instruments such as imaging radar, infra-red, visible, etc. Or automated response might be used to apply more assets to increase the frequency of observation to improve the temporal resolution of available data. Our sensorweb project is being used to monitor the Earth's 50 most active volcanos. We have also run sensorweb experiments to monitor flooding, wildfires, and cryospheric events (snowfall and melt, lake freezing and thawing, sea ice formation and breakup.)
Topic area: Science
Development stage: Deployed
First used: 2003-01-01
US federal AI use case: Mexec Onboard Planning and Execution
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: MEXEC is a lightweight, multi-mission software for activity scheduling and execution developed to increase the autonomy and efficiency of a robotic explorer. MEXEC was first created as a prototype demonstration for the Europa Clipper project as a potential solution to fail-operational requirements.
Purpose and benefits: Instead of command sequences, MEXEC works with task networks, which include abstract representations of command behavior, constraints on timing, and resources required and/or consumed by the behavior. Using this knowledge on-board, MEXEC can monitor command behavior and react to off-nominal outcomes (e.g. CPU reset), reconstructing command sequences to continue spacecraft operations without jeopardizing spacecraft safety.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Requirement Alignment (REQAL)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Use NLP and large language Model (LLM) such as GPT or BERT to encode experiences of the personnel into a knowledge graph. Then train a graph neural network (GNN) to automatically match solicitations to experts in the organization.
Purpose and benefits: Use NLP and large language Model (LLM) such as GPT or BERT to encode experiences of the personnel into a knowledge graph. Then train a graph neural network (GNN) to automatically match solicitations to experts in the organization.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: This effort aims to improve the science yield and robustness of a wide range of NASA missions by increasing the level of flight-qualifiable autonomy that can be applied to such operations.
Purpose and benefits: Specifically, we are developing autonomy for onboard mission use that could detect, diagnose, and respond appropriately to anomalies (faults, failures degradations, or unexpected conditions) without the need to always drop into safe mode and call home.
Topic area: Science
Development stage: Pre-deployment