Question 1.

AI Use Case Identifying Information

Question 3.

 Summary

Question 4.

 Lifecycle Stage

Question 5. (Optional)

 AI Techniques 

Question 6. (Optional)

 Data Approach

Question 7. 

Technical 

(Optional) 

Solution

Question 8. (Optional)

Information System Use 

Question 9.

Case Releasability

AI use 

1A.

case name Agency 

1B.

with AI use case

Optional Note Field: To clarify 

1B or to provide additional 

information (e.g. Agency codevelopment) 

Office 

1C.

with AI use case Provide a short summary (200 words max) 

description of system 

of what the AI does. 

inputs and outputs. 

Include a high-level What stage of 

4A.

production 

in?

is the AI Additional 

4B.

comments related 

stage.

to lifecycle What specific AI techniques were used? Did/does 

6A.

the agency train 

use case?

this AI If yes - Where 

6B.

did/does the 

originate?

training data 

6C.

Is the training data, the validation 

data, and/or test data included in 

the enterprise data inventory? 

If data is 

6D.

publicly available, 

link. 

provide 

7A.

Does the agency have access to the 

code associate with this AI use 

case?

If yes, 

agency 

7B.

is the code included in 

source code inventory 

Code.gov)?

the 

(e.g. 

If the 

7C.

source code is publicly 

provide the link.

available, Is the 

7D.

agency able to conduct 

testing on the code?

ongoing 

7E.

Is the agency able to monitor 

and/or audit performance? 

Provide 

FISMA 

the name of the Information System 

system name) associated with the AI 

case.

(e.g. 

use 

9A.

Should this use case be withheld from the public 

inventory? If yes, the use case will only be shared in 

the internal government inventory. 

9B.

 If the answer to 9A is yes, provide an explanation (this 

explanation will be included in the internal government 

inventory).

Accelerated discovery in TESS full frame images 

using machine learning

Autoresolver/Tailored Arrival Manager

ExoMiner discovery of ExoPlanets via data from the 

Kepler spacecraft

Explainable and robust deep semi-supervised model 

for multi-class anomaly detection in flight data

IrrMapper Computer Vision

Unsupervised anomaly detection in flight data with 

deep variational autoencoders

Bone Mineral Density Maintenance During LongDuration Spaceflight

Toward a deep-learning-based cloud-masking 

framework for enhanced monitoring of water 

resources 

IMERG Hydro-GV at the catchment scale

Machine Learning Approaches for Predicting 

Phytoplankton Community Composition from Ocean 

Color

AEGIS: Autonomous Exploration for Gathering 

Increased Science

ASPEN Mission Planner

CLASP Scientific Instrument Planning & Scheduling

Enhanced AutoNav for Perseverance Rover on Mars

Mars2020 Rover (Perseverance)

MLNav (Machine Learning Navigation)

Perseverance Rover on Mars

Planetary Cave Rovers

SCOTI (Scientific Captioning of Terrain Images)

SPOC (Soil Property and Object Classification)

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

National Aeronautics

Administration

and

and

and

and

and

and

and

and

and

and

and

and

and

and

and

and

and

and

and

and

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Space

Goddard Space Flight Center

Ames Research Center

Ames Research Center

Ames Research Center

Ames Research Center

Ames Research Center

Glenn Research Center

Goddard Space Flight Center

Goddard Space Flight Center

Goddard Space Flight Center

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Jet Propulsion Laboratory

This work uses convolutional neural networks to accelerate discovery in TESS (Transiting

Exoplanet Survey Satellite) data in the form of full frame images. Techiquesinclude 

recognizing variancesin light curves from stars and identifying patterns which might

indicate planets orbiting them.

The Autoresolversystem 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

locationsin space, and avoiding airspace volumeslike weathersystems and restricted

airspace. The tool relies on search/optimization algorithmsto select safe and efficient

routes for aircraft, aircraft physics models to predict future aircraft states, and probabilistic

models of the accuracy of these trajectory predictions to inform resolution selection.

Machine learning, statistics, and human SME interpretation combined to validate over 300

newly-discovered exoplanets(and growing). The machine learning models were trained via

robust human-labelled data, specifically the patterns in star brightness introduced when a

planet traverses between a star and NASA sensors. These models were trained, tested and

validated, and have now been put into operational use with excellent highly accurate 

results.

This model is a semi-supervised deep learning based anomaly detection for aircraft flight 

data. It is designed to work when a small subset of data is reviewed and labeled by experts. 

The most useful realm is where the size of labeled data is small, so that any supervised 

learning approach won't reach optimum performance. It is also trained with customdesigned objective function so the learned features are explainable for down-stream tasks 

such as active learning (data selection for future labeling) and it is tested to be robust to 

adversarial perturbation. It is currently tested and validated in finding anomaly detection in 

flight's operational quality assurance data from commerical aircraft.

High frequency and spatially explicit irrigated land maps are important for understanding 

the patterns and impacts of consumptive water use by agriculture. This work created an 

extensive database of land cover across 50,000 irrigated fields, 38,000 dryland fields, and 

500,000 square KM of uncultivated lands. The team used 28 years of LandSAT data to 

create a land-type classifier that provides 97.8% accurate results in classifying irrigated vs. 

un-irrigated land. 

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. It is currently tested and validated in finding anomaly detection in flight's operational 

quality assurance data from commerical aircraft.

Bone mineral density maintenance is important for astronauts on long-duration missions. 

This work uses probabilistic image segmentation to automatically generate bespoke finite 

element models of femoral bones from CT scan images in combination with in-the-loop 

stochastic optimization to determine loading forces and frequencies required to maintain 

bone mineral density in microgravity. This information can be used by flight physicians to 

design customized exercise routines for astronauts during spaceflight using on-board 

resistive exercise devices.

To produce reliable aquatic science products using multispectral satellite imagery, 

preprocessing steps are often necessary to remove clouds and cloud shadows. These 

contaminations can lead to highly inaccurate aquatic reflectance spectra and 

downstream products that may misinform decision-making processes (e.g., issuing 

advisory). As of yet, various image processing workflows apply simple thresholding 

approaches; however, they lack fidelity in dealing with thin clouds or cloud shadows 

or when seamless multi-mission products are desired. Machine learning approaches 

have shown promise in cloud-masking tasks but require extensive training sets, which 

are primarily available over terrestrial targets. This effort aims to create a framework 

for a robust cloud-masking of existing satellite images over inland and coastal waters. 

To exclude cloud and cloud shadows, a domain adaptation-based deep learning model 

for semantic segmentation of satellite images is developed. Hand-labeled training 

samples are generated from Landsat-8/OLI and Sentinel-2/MSI imagery to cover a 

broad latitudinal band across the Americas.

IMERG (integrated multi-satellite retrievals for GPM (Global Precipitation Measurement)) 

Hydro-GV at the catchment scale. The Integrated Multi-satellitE Retrievals for GPM (IMERG) 

algorithm combines information from the GPM satellite constellation to estimate 

precipitation over the majority of the Earth's surface. Techniques include support vector 

machine ML models.

This work uses Bayesian ML approaches to predict phytoplankton community composition 

(PCC) from in situ and satellite measurements of ocean color. PCC reveals fundamental 

insight into oceanic carbon cycling and ecosytem functioning, making its determination vital 

for understanding a changing ocean. The models are trained using state of the art 

measurements of PCC (e.g., imaging flow cytometry and molecular assays), which sets it 

apart from traditional phytoplankton pigment based models.

AEGIS enables intelligent targeting and data acquisition by planetary rovers. It uses 

computer vision techniques to identify targets (e.g., rocks) in wide angles images of the 

rover's surrounding terrain. If targets are found that match scientists specificiations, they 

are then measured autonomously using remote sensing instruments. AEGIS was first used 

on the MER Mission. It is currently in use on the MSL Mission to acquire data for the 

ChemCam instrument. It is planned for use in Spring of 2022 on the M2020 Mission to 

acquire data for the SuperCam instrument.

Based on AI techniques, ASPEN is a modular, reconfigurable application framework which is 

capable of supporting a wide variety of planning and scheduling applications. ASPEN 

provides a set of reusable software components that implement the elements commonly 

found in complex planning/scheduling systems, including: an expressive modeling language, 

a resource management system, a temporal reasoning system, and a graphical interface. 

ASPEN has been used for many space missions including: Modified Antarctic Mapping 

Mission, Orbital Express, Earth Observing One, and ESA's Rosetta Orbiter. 

The Compressed Large-scale Activity Scheduling and Planning (CLASP) project is a long-range 

scheduler for space-based or aerial instruments that can be modelled as pushbrooms -- 1D 

line sensors dragged across the surface of the body being observed. It addresses the 

problem of choosing the orientation and on/off times of a pushbroom instrument or 

collection of pushbroom instruments such that the schedule covers as many target points as 

possible, but without oversubscribing memory and energy. Orientation and time of 

observation is derived from geometric computations that CLASP performs using 

the SPICE ephemeris toolkit. CLASP allows mission planning teams to start with a baseline 

mission concept and simulate the mission's science return using models of science 

observations, spacecraft operations, downlink, and spacecraft trajectory. This analysis can 

then be folded back into many aspects of mission design -- including trajectory, spacecraft 

design, operations concept, and downlink concept. The long planning horizons allow this 

analysis to span an entire mission. Actively in use for optimized scheduling for the 

ECOSTRESS mission (study of water needs for plant areas), EMIT mission (minerology of arid 

dusty regions), OCO-3 (atmospheric CO2) and more.

AutoNav on the Perseverance Rover autonomously plans a safe path based on stereo 

navigation camera images, based on multiple technologies including a tree search for 

decision making, Dijkstra algorithm for global path planning, stereo processing for 3D 

terrain reconstruction, and Approximate Clearance Evaluation (ACE) for safety checks. It is 

deployed on Perserverance rover and being used for autonomous driving on Mars.

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. The M2020 onboard scheduler incrementally constructs a feasible 

schedule by iterating through activities in priority-first order. When considering each 

activity it computes the valid time intervals for placement, taking into account preheating, 

maintenance heating, and wake/sleep of the rover as required. After an activity is placed 

(other than a preheat/maintenance or wake/sleep), the activity is never reconsidered by the 

scheduler for deletion or moving. Therefore the scheduler can be considered non 

backtracking, and only searches in the sense that it computes valid timeline intervals for 

legal activity placement. Meta Search: Because the onboard scheduler will be invoked 

many times in a given sol (Martian Day) with a range of possible contexts (due to execution 

variations), its non backtracking nature leaves its vulnerable to brittleness. In order to 

mitigate this potential brittleness, the Copilot systems perform a monte carlo based 

stochastic analysis to set meta parameters of the scheduler - primarily activity priority but 

also potentially preferred time and temporal constraints.

Accelerates path planning of rovers and other types of vehicles through ML-based 

heuristics, while guaranteeing safety through conventional, model-based collision checking. 

Integrated with M2020's Enhanced AutoNav (ENav) and tested with the real terrain data 

from Mars on ENav simulator. Uses U-net for the ML-based heuristics, trained by simulationgenerated terrain data. 

3D machine vision via dual cameras to inform convolutional neural networks for rover 

navigation path planning. Physics / momentum - based Terrain Relative Navigation (TRN). 

Enhanced AutoNavigation (Enav) plans feasible paths using multiple techniques, to include 

random forests. Approximate Clearance Evaluation (ACE) assesses obstacles to deterimine 

if the suspension of the rover can clear them (drive over them) or needs to route around 

them. Additional, more complex AIML techniques are being planned and tested for future 

rovers at JPL. TRN was also used to provide precision landing for the Perseverance Rover in 

the entry, descent, and landing process to get the rover to the surface of Mars.

In this project, we study multi-rover coordination techniques to allow vehicles to 

autonomously explore the unknown environments of caves on Mars and the Moon. Since 

the ultimate goal is to return science data, data routing becomes a key design point. To 

analyze our different techniques, we developed a simulation framework to easily compare 

different mission configurations. We developed a simulation framework to easily run 

different configurations for mission concepts, which provides diagnostic output to evaluate 

performance. It is implemented in the Robot Operating System (ROS) and allows 

configuration of asset parameters, environmental parameters, and mission concept 

parameters, including the number of assets, asset specifications (such as the 

communication model, navigation model, exploration strategy, and science instruments), 

and the cave model. Diagnostic output includes an interactive visual playback of the 

scenario, activity timeline and distribution, paths travelled by the assets, and energy usage 

distribution.

SCOTI (Scientific Captioning of Terrain Images) automatically generates natural language 

explanations of geological images taken by rovers. It uses "sohw-attend-tell" model 

consisting of CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory), 

trained by scientist-genarated labels on MSL images. SCOTI provides onboard data 

summarization tha would help the ground operation to selectively downlink high priority 

data under data bandwidth constraint.

Using a convolutional neural network (CNN), SPOC (Soil Property and Object Classification) 

takes rover images and classifies the terrain type (e.g., sand, soil) from visual appearance. 

This ability enables rover to drive more safely. It is trained by labeled images from MER 

(Mars Exploration Rover), MSL (Mars Science Laboratory), and Mars 2020 rovers, annotated 

by tens of thousands of citizen scientsts through the AI4Mars project. SPOC deployed on 

MSL's ground operation system and onboard test on M2020 is being considered. 

Prototyped for limited data

environment

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

In production: more than 1 year

See PI

Awaiting continued funding from HRP to resume 

production.

See PI

See PI

Used by multiple satellites and sensors for 

multiple years

Used by multiple satellites and sensors for 

multiple years

Deployed on Mars.

Deployed on Mars

Sim environment is in place to experiment with, 

develop, and test AI-enabled systems to enable 

autonomous cave exploration

Deployed on MSL ground ops; being considered 

for onboard demonstration on M2020

Convolutional Neural Network 

Probabilistic (bayesian), Bayesian Network, 

Uncertainty

Machine learning pattern recognition

trained with human-labelled data

Deep Neural Networks

Big data ML classifiers

Deep Neural Networks, Variational 

Inference

Naïve Bayes Classifiers, Stochastic 

Optimization

Domain adaptation deep learning 

Support-vector machine (SVM)

Artificial Neural Network, Dirichletmultinomial process

computer vision

Stochastic Local Search scheduler taking 

into account numerous criteria and 

variables

Squeaky wheel optimization scheduler 

taking into account numerous criteria and 

variables

Tree search, path planning, stereo 

processing, approximate kinematic solver

Automated scheduling, Meta Search, 

Autonomous planning, image processing, 

terrain classification, fault diagnosis

Convolutional Neural Netowrk (CNN), UNet CNN

Convolutional Neural Networks, Random 

Forests,Terrain Relative Navigation

On-board autonomy, multi-rover 

coordination, planning, scheduling and 

execution

CNN, LSTM

CNN

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

Yes

Yes

Yes

Yes

NASA

Agency

Agency

Agency

Other

Other

Agency

Agency

Agency

Other

Generated

Generated

Generated

Generated

Generated

Generated

prototype,

Yes

Yes

No

No

No

No

No

No

No

still in development https://archive.stsci.edu/tess/bulk_do prototype,

wnloads/bulk_downloads_ffi-tp-lcdv.html

Yes

https://c3.ndc.nasa.gov/dashlink/projec No

https://www.mdpi.com/2072-

4292/12/14/2328

https://c3.ndc.nasa.gov/dashlink/projec No

Yes

No

Yes

Yes

Yes

Note: this is NOT ML-based Yes

Yes

Yes

Yes

Yes

https://data.nasa.gov/Space-Science/AI4Yes

still in development N/A

No

No

No

No

N/A N/A

Yes

No

N/A

No

N/A No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

ts/85/

ts/85/

MARS-A-Dataset-for-Terrain-Aware-Autonomous-Dri/cykx-2qix
TRN (Terrain Relative Navigation) National Aeronautics and Space Jet Propulsion Laboratory Terrain Relative Navigation (TRN) estimates position during Mars landing by automatically In production: more than 1 year TRN is planned for use in the next NASA mars Computer vision and state estimation. Yes No

Administration matching landmarks identified in descent images to a map generated from orbital imagery. lander mission.

The position estimate is used to a select a safe and reachable landing site in a region with 

many large hazards. TRN was used successfully by the Mars 2020 mission during its landing 

on February 18th, 2021.

Volcano SensorWeb National Aeronautics and Space Jet Propulsion Laboratory The Sensor Web Project uses a network of sensors linked by software and the internet to an In production: more than 1 year Automatic Data Interpretation, Planning Yes No N/A

Administration autonomous satellite observation response capability. This system of systems is designed and Scheduling, Multi-Sensor Fusion

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.) 

Machine Learning for RFID (Radio Frequency National Aeronautics and Space Johnson Space Center Currently have two production machine learning approaches to tackle RFID (Radio In production: more than 1 year Derivitive work are in various stages of Deep Neural Network, transfer learning, Yes Agency Generated No Yes No Yes Yes No Data from this use case has potentially derived sensive 

Identification) tag localization to support logistics Administration Frequency Identification) tag localization in the highly reflective environment imposed by development. date image generation, image classification, information.

the International Space Station. First use case, REALMRFC, is a random forest classifier unsupervised learning, semi-supervised 

model with feature engineering performed by an RFID localization expert. The second use learning, decision tree classifiers, K-nearest 

case is P-RFIDNet, a neural network with a ResNet50 backbone. In continued work, we have neighbors classifiers, statistical methods

leveraged transfer learning to show how P-RFIDNet can be generalized to new RFID 

environments with limited training data. We benchmark P-RFIDNet and REALMRFC using 

data from the RFID Enabled Autonomous Logistics Management (REALM) and using truth 

derived from the Inventory Management System (IMS).

NASA OCIO STI Concept Tagging Service National Aeronautics and Space Langley Research Center An API (application program interface) for exposing topic models created with the STI In production: more than 1 year NLP Yes Yes https://github.com/nasa/concept-tagging-api No N/A

Administration (Scientific & Technical Information) concept training repository.

Watson Explorer National Aeronautics and Space Langley Research Center Langley Research Center has used IBM Watson Explorer (WEX) Natural Language Processing In production: more than 1 year Natural language processing No

Administration for over 8 years to analyze numerous text-based data sets. The capability provides cognitive 

exploration and content analysis and is used to explore and analyze structured and 

unstructured, internal, external and public content to uncover trends and patterns. The 

team has applied WEX to data sets up to 600,000 documents to cluster concepts, conduct 

author analysis, and explore the data via powerful faceted search

INtelligent StennIs Gas House Technology (INSIGHT) National Aeronautics and Space Stennis Space Center INSIGHT is an operational system that performs autonomous Integrated System Health In production: more than 1 year INSIGHT is currently at a base autonomy INSIGHT employs the AI technologies No Yes No Yes Yes No

Administration Management (ISHM) and autonomous operations of the Nitrogen System of the High capability, and the expectation is to continue its provided by NPAS, described in this 

Pressure Gas Facility at NASA Stennis Space Center. It is an application implemented using evolution to achieve high levels of autonomy document as AI Use Case Name: NASA 

the NASA Platform for Autonomous Systems (NPAS) described in this document as AI Use capability. INSIGHT represents a validation of the Platform for Autonomous Systems (NPAS).

Case Name: NASA Platform for Autonomous Systems (NPAS). The inputs are (1) descriptions AI technologies of NPAS for operational systems 

of system structure and behavior including schematics, (2) description of operations, (3) that have to meet safety criticality criteria and 

autonomy strategies to mitigate off-nominal behavior, (4) strategies for ISHM (anomaly requirements. INSIGHT has been classified as Class 

detection, diagnostics, prognostics, comprehensive awareness. C Safety Critical.

NASA Platform for Autonomous Systems (NPAS) National Aeronautics and Space Stennis Space Center 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. AI behavior, 

"thinking," is grounded in a comprehensive representation of the system (comparable to 

SysML model descriptions that include health management and autonomy behaviors as 

well as schematic level descriptions), behavior/function models (physics-based, heuristic, 

rule-based - probabilistic and neural-network models can also be incorporated), Failure 

Modes and Effects Analysis (FMEA) with generic/re-usable libraries. 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.

In production: more than 1 year NPAS has been used to implement autonomous 

operations of the Nitrogen System of the High 

Pressure Gas Facility (HPGF) at Stennis Space 

Center. NPAS is currently being used to implement

autonomous satellite capability under the AES 

Polaris project "Autonomous Satellite Technology 

for Resilient Applications (ASTRA)," in 

collaboration with commercial partner Sidus 

Space. NPAS was used to prototype hierarchical 

distributed autonomous operations of a Vehicle 

System Manager, multiple module managers, and 

system managers; according to the Gateway 

autonomy design. This was a risk reduction 

collaboration with commercial partner Lockheed 

Martin.

On-board "thinking" by the system rather 

than on-board application of "thinking" 

that is done off-line by teams of humans. 

The AI technologies embody a transfer of 

the thought and process capabilities of 

humans to the system itself. Model-based 

on-board "thinking" using models of 

existing knowledge (e.g. physics models, 

heuristic models, etc.), processes (e.g. 

engineering and science processes, 

strategies for health management and 

autonomy), Failure Modes and Effects 

Analysis (FMEA) with generic cause-effect 

tree representation and resolution for 

failure instances occurring in real-time, goalbased task management including task 

definition, planning, scheduling, and 

execution . In general, thinking that 

involves perception, analysis, evaluation, 

reasoning, decision making, task and 

mission execution.

No Yes No Not publicly available Yes Yes No

Administration

High-Performance Quantum-Classical Hybrid Deep 

Generative Modeling Parameterized by Energybased Models for Flight-Operations Anomaly 

Detection

National Aeronautics and Space 

Administration

Ames Research Center Our project conducts high-performance scalable and explainable machine learning for flightoperations anomaly detection, with contributions from classical computing (enhanced 

performance, reduced cost) and quantum computing (encoding of quantum correlations, 

quantum-resource estimates). Our deep-learning model takes time series of 19 flight metrics 

collected by flight recorders of commercial aircraft as input and predicts operational and 

safety-relevant anomalies during the take-off and landing phases of flight.

In production: less than 6 months Deep learning, generative modeling, semisupervised learning, model explainability, 

active learning, quantum regularization

Yes Agency Generated Yes https://c3.ndc.nasa.gov/dashlink/projects/85/ No N/A

Integrated System for Autonomous and Adaptive 

Caretaking (ISAAC)

National Aeronautics and Space 

Administration

Teamwork with Johnson Space 

Center

Ames Research Center In production: less than 6 months Semantic classification, Automated Yes Agency Generated Yes No

 Human exploration infrastructure in deep space, such as the planned “Gateway”, will be 

uncrewed for extended periods. During these periods NASA needs autonomous systems 

that can perform Fault Detection, Isolation, and Recovery (FDIR) as well as perform routine 

maintenance and logistics operations through high latency communication to ground 

controllers. The Integrated System for Autonomous and Adaptive Caretaking (ISAAC) project 

directly addresses this key technical need.

planning, Automated change and anamoly 

detection 

Cognitive Communications National Aeronautics and Space 

Administration

Glenn Research Center In production: less than 6 months See PI Yes Agency Generated No No Data and Code to be made available upon p Yes Yes No N/A

The Cognitive Communications project will design, build, and demonstrate a cognitive 

communication system prototype called Cognitive Engine – 1 (CE-1). CE-1 will seamlessly 

operate with NASA and commercial networks, eliminate the need for operators to forecast 

communication service schedules, adapt to changing environmental conditions without 

human interaction, and maximize data throughput. CE-1 is a ground-based prototype 

cognitive system capable of handling communication tasks for emulated spacecraft, groundbased network assets, and mission operation centers. CE-1 represents NASA’s first step 

towards enabling autonomous spacecraft communications. The project will assemble a CE1 prototype by incrementally combining AI/ML and communication technologies that 

deliver automated, reactive, predictive, and finally cognitive functionality. 

Reinforcement Learning, Deep Learning, 

Generative Adversarial Networks, Spiking 

Neural Networks Some services run on Goddard Commercial Cloud AWS 

Graph Neural Networks for Airfoil Performance National Aeronautics and Space Glenn Research Center 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. 

The impact is we have a new type of neural network architecture that we can potentially 

use for other projects.

In production: less than 6 months Software safety is currently holding up the project Graph Neural Networks, Spline Convolution Yes Agency Generated https://nasa-public-data.s3.amazonaws.com/plot3d_utilities/airfoil-learning-dataset.zip No N/A

Prediction Administration from being released.

Inverse Design of Materials National Aeronautics and Space Glenn Research Center 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. The team is using supervised machine learning techniques to 

develop ML models of material structure based on 100,000+ NASA images, to include using 

ML to optimize materials recipes based on desired materials properties. ML models are also 

acting as surrogate models, with 100 to 10,000 times speedup over traditional physics 

simulations already realized for select materials. Inputs include materials images, 

metadata, and SME-based labelling. Outputs include recipes and approaches for new 

materials custom-tailored to applications with an 4x speedup for the overall materials 

discovery / design lifecycle, and potential 10x throughput for the same cycle based on 

parallizing discovery of multiple materials at once.

In production: less than 6 months This project is in the process of iterative 

development between data scientist and SME. It 

is beyond "planned," but not "in production" yet.

Machine learning image recognition, 

Supervised ML technigues

Yes Agency Generated Yes Yes No Data and Code to be made available upon 

project completion

Yes Yes This system is running on cloud and on-premise systems 

behind the NASA firewall

No N/A

Administration

Physics Based Neural Networks for Fluid Dynamics National Aeronautics and Space Glenn Research Center Physics based neural networks are a new field. They can be used to predict CFD 

(computational fluid dynamics) results. One of the main advantages is that they do not 

need a mesh, they can solve the navier stokes using randomly simulated points. This makes 

it advantageous in cavity flow, narrow passages, turbine blade tip geometries, just to name 

a few. Since this is a new field, it is important that we advance the science to improve 

accuracy.

In production: less than 6 months Physics Based Neural Networks with Deep 

Neural Network archtecture

Yes Agency Generated No N/A

Administration

Probe Calibration using Neural Networks National Aeronautics and Space 

Administration

Glenn Research Center Sensor / probe calibration for wind tunnel testing has historically relied on maps and 

surfaces. Interpolation is also difficult because of the multi-dimensionality of the 

calibration data. Calibration data includes the angles that the probe can experience in pitch 

and yaw, in addition to the different test conditions. Machine learning can be used to 

better estimate the multi-dimensionality of the dataset resulting in an accurate pitch and 

yaw angles based on what the probe is experiencing in the test section.

In production: less than 6 months Deep Neural networks Yes Agency Generated No N/A

Using a ML regression model for processing National Aeronautics and Space 

hyperspectral observations over optically complex Administration

aquatic ecosystems

Goddard Space Flight Center In production: less than 6 months Coastal and freshwater ecosystems are among the most productive ecosystems. They 

are highly vulnerable to the changing climate and to anthropogenic activities while 

supporting human lives through the services they provide. For over two decades, satellite 

systems with ocean color (OC) capabilities have produced valuable records of 

observations for studying the carbon and biogeochemical cycling in these optically 

complex environments. In preparation for NASA’s Surface Biology and Geology mission, 

this project aims at developing and demonstrating a processing pipeline which utilizes 

Mixture Density Networks (MDNs) to enable high-quality retrievals of aquatic products 

from imaging spectrometry data. Our retrieval scheme constitutes two steps. First, we will 

conduct an atmospheric correction to retrieve hyperspectral remote sensing reflectance 

using an MDN model trained with in situ Rrs and simulated top-of-atmosphere reflectance. 

Second, we will utilize the derived Rrs products to simultaneously retrieve in-water 

properties. Our framework will be implemented for, and validated with, Hyperspectral 

Imager for Coastal Ocean (HICO) images using the AERONET-OC data and in situ data 

acquired at well-monitored U.S. coastal estuaries and lakes. To further extend our 

validation opportunities, we will also demonstrate our retrieval scheme for the Italian Space

Agency’s existing hyperspectral mission (PRISMA). 

See PI No No No https://github.com/STREAM-RS Yes Yes No

A class of NNs referred to as Mixture 

Density Networks (MDNs) The assembled training dataset has been submitted to Nature's Scientific Data for

High Resolution Earth and Planetary Atmospheric 

Predictions using Machine Learning

National Aeronautics and Space 

Administration

Goddard Space Flight Center We are developing a machine learning (ML) tool that can predict high-resolution in situ 

atmospheric conditions using relatively lower resolution remote data e.g., from an orbiting 

spacecraft. Our ML tool could be used to map and track atmospheric cycling on Earth and 

planetary bodies, not only as a fundamental science tool, but also as a mechanism for 

tracking planetary weather from orbit.

In production: less than 6 months Autoencoders, convolutional neural Yes Agency Generated No N/A

network, UMAP

Autonomous Marine Vehicles (Single, Multiple) National Aeronautics and Space 

Administration

Jet Propulsion Laboratory Due to the communication paradigm associated with operating an underwater submersible 

on an Ocean World, the vehicle must be able to act autonomously when achieving scientific 

goals. One such goal is the study of hydrothermal venting. Evidence for hydrothermal 

activity has been found on one Ocean World, Enceladus. On Earth, these geological 

phenomena harbor unique ecosystems and are potentially critical to the origin of life. 

Similar vents on Ocean Worlds could be the best chance at extra-terrestrial life in our Solar 

System. We focus on performing autonomous science, specifically the localization of 

features of interest - such as hydrothermal venting - with limited to no human interaction. 

A field program to Karasik Seamount in the Arctic Ocean was completed in Fall 2016 to 

study and understand the human-in-the-loop approach to the localizing hydrothermal 

venting. In 2017/2018 an autonomous nested search method for hydrothermal venting was 

developed and tested in simulation using a hydrothermal plume dispersion model 

developed by Woods Hole Oceanographic Institution

In production: less than 6 months Testing and experimention with a variety of 

techniques is underway

Planning and Execution, Nested Search, 

Anomaly Detection

No N/A

roject completion 

 peer review
Hybrid On-Board and Ground-Based Processing of 

Massive Sensor Data (HyspIRI IPM)

Neural network accelerated radiative transfer 

modeling

Intelligent Contingency Management

Lessons Learned Bot (LLB)

Pedestrian Safety Corridors for Drone Test Range

Storm Prediction via Above Anvil Cirrus Plume Image 

Recognition

Superimposition and Collation of Images with NonEnglish Translations (SCI-NET)

National Aeronautics 

Administration

National Aeronautics 

Administration

National Aeronautics 

Administration

National Aeronautics 

Administration

National Aeronautics 

Administration

National Aeronautics 

Administration

National Aeronautics 

Administration

and 

and 

and 

and 

and 

and 

and 

Space 

Space 

Space 

Space 

Space 

Space 

Space In teamwork with 

Flight Center

Marshall Space 

Jet Propulsion Laboratory

Jet Propulsion Laboratory

Langley Research Center

Langley Research Center

Langley Research Center

Langley Research Center

Langley Research Center

Future space missions will enable unprecedented monitoring of the Earth's environment 

and will generate immense volumes of science data. Getting this data to ground 

communications stations, through science processing, and delivered to end users is a 

tremendous challenge. On the ground, the spacecraft's orbit is projected, and automated 

mission-planning tools determine which onboard-processing mode the spacecraft should 

use. The orbit determines the type of terrain that the spacecraft would be overflying—land, 

ice, coast, or ocean, for instance. Each terrain mask implies a set of requested modes and 

priorities. For example, when a spacecraft overflies polar or mountainous regions, 

producing snow and ice coverage maps can provide valuable science data. The science team 

can adjust these priorities on the basis of additional information (such as external 

knowledge of an active volcano, a flooded area, an active wildfire, or a harmful algal 

bloom). The mission-planning tool accepts all these requests and priorities, then determines 

which onboard-processing algorithms will be active by selecting the highest-priority 

requests that fit within the onboard CPU resources, band-processing limitations, and 

downlink bandwidth.In the intelligent onboard processing concept, HyspIRI's onboard 

processing algorithms would consist of expert-derived decision tree classifiers, machinelearned classifiers such as SVM classifiers and regressions, classification and regression trees 

(CART), Bayesian maximum-likelihood classifiers, spectral angle mappers, and direct 

implementations of spectral band indices and science products

Started in FY21 the AI behind the Neural network accelerated radiative transfer modeling is 

intended to enhance efforts in the Earth Science domain. Specifically, JPL constructed a 

flexible radiative transfer model (RTM) that combines physics-based models and artificial 

neural networks with the intent of providing fast radiative transfer modeling for global 

imaging spectroscopy missions, as well as large-scale airborne campaigns (ABoVE, Western 

Diversity Time Series, FIREX-AQ, etc.)

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. Inputs include sensors for aircraft state and the 

surrounding environment (natural and man-made). Outputs include recognition of offnominal conditions (contingencies) and mission executon strategy adjustments.

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. 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. The 

LLB’s installation package comes with a pre-trained NASA LL dataset and a NASA Scientific 

and Technical Information (STI) dataset, as well as on-demand training tools allowing the 

user to apply the LLB search algorithm to their own discipline specific datasets.Additionally, 

we also have an API version of this software that can be called from any application within 

the Agency firewall. 

NASA Langley Research Center (LaRC) is actively experimenting with Unmanned Aerial 

Systems (UAS - Drones and surrounding systems) to include command, control, coordination 

and safety mechanisms. LaRC is expanding an on-site UAS test range, to include areas 

where people walk, drive, etc. This project leverages the parking advisor image recognition 

project and applies it to detecting pedestrian traffic to supplement statistical assessment of 

human-heavy and human lite traffic areas with near-real time human-presence-detection. 

Inputs include camera signals and hand labelled training data. Outputs include maps 

indicating density of human pedestrian traffic.

Above-Anvil Cirrus Plumes (AACPs) are a precursor to destructive hailstorms. This project 

seeks to train machine learning image recognition techniques to identify AACPs in satellite 

imagery to eventually contribute to an early storm warning system. The system uses image 

recognition, U-Nets, and Long-Term-Short-Term memory systems. Inputs include satellite 

imagery and human labeling of AACPs. Outputs include machine-identified AACP instances.

NASA seeks to leverage global aerospace knowledge, and many key concepts lie in historical 

technical literature in foreign languages. This project leverages ever-improving language 

translation capabilities from industry to translate Russian and other languages to English, 

and then overlay the translated text over the original document, maintaining 

synchronization with figures, diagrams, graphs, formulas, etc. Inputs include foreign 

language technical documents as well as SME interpretation of jargon. The primary output 

is the translated text overlaid over the other document elements from the original 

document.

In 

In 

In 

In 

In 

In 

In 

production: 

production: 

production: 

production: 

production: 

production: 

production: 

less 

less 

less 

less 

less 

less 

less 

than 

than 

than 

than 

than 

than 

than 

6 

6 

6 

6 

6 

6 

6 

months

months

months

months

months

months

months

In prototyping and experimentation

This project is in the process of iterative 

adaptation, development, and training within a 

complex, detailed modeling and simulation 

environment. It is in the applied R&D stage (under 

development); this means that it is not "planned," 

but not yet in production operations either.

Currently in production and recieves 

updates/security patches

This project is in the process of iterative 

development between data scientist and SME. It 

is beyond "planned," but not "in production" yet.

This project is in the process of iterative 

development between data scientist and SME. It 

is beyond "planned," but not "in production" yet.

This project is in the process of iterative 

development between data scientist and SME. It 

is beyond "planned," but not "in production" yet.

decision tree classifiers, classification & 

regression trees, Baysean maximum 

likelihood classifiers, spectral angle 

mappers, support vector machine classifiers

Neural networks

Reinforcement Learning, Knowledge 

Reasoning, Probabilistic Inference, 

Generative Adversarial Networks, PhysicsInformed Neural Networks

Doc2Vec NLP Machine Learning Approach

Image recognition, Recurrent convolutional 

neural networks, Perspective-based grid 

generation system to account for parallax 

and similar phenomena

Image recognition, U-Nets, LSTM Neural 

Networks and more

Foreign language translation and document 

element recognition systems

Yes

Yes

Yes

Yes

Yes

Yes

Agency 

Agency 

Agency 

Agency 

Agency 

Other

Generated

Generated

Generated

Generated

Generated

Yes

Yes

No

Yes

Yes

https://ntrs.nasa.gov/

https://llis.nasa.gov/

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

Data and Code will be made available 

upon project completion

Code to be made available upon project 

completion; imagery data is inappropriate 

to share however due to physical security 

concerns.

Data and Code will be made available 

upon project completion

From Google Cloud Platform AIML services

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

This is running in a firewalled NASA Aeronautics 

modeling and simulation system.

llb.larc.nasa.gov

This system is running on a firewalled on-premesis server 

in a local data center

This system is running on a firewalled cloud R&D 

environment

These services run on Google Cloud Platform; NASA's 

instance is part of a firewalled GovCloud instance.

No

No

No

No

No

No

No

N/A

N/A

N/A

N/A

N/A

N/A

Dynamic 

safety 

Lightning Prediction Algorithm for lightning National Aeronautics and Space Marshall Space Flight Center Uses a long short-term memory model and convoloutional neural network to predict In production: less than 6 months Neural network long-short-term memory Local ST11 machine No

Administration lightning occurrence and extent in for a short term 5-15 minute period. models and convolutional neural networks.

Sinatra National Aeronautics and Space Marshall Space Flight Center A flexible software framework that analyzes big data and detects anomalies. Allows for In production: less than 6 months Framework for big data analysis and No

Administration integrating ML with minimal background in deep learning and software development. anomaly detection

Deep Learning for asteroid damages prediction National Aeronautics and Space Ames Research Center This project is focused on prediction of ground damages of different asteroid impact In production: less than 1 year Deep Neural networks Yes Agency Generated Yes Yes No https://github.com/gregoirechomette/ml-atap.git No N/A

Administration scenarios based on deep neural network. The training data used comes from millions of 

numerical simulations based on the physics - the neural networks aim at providing a lower 

fidelity but fast prediction of the damages.

Guided Materials Discovery National Aeronautics and Space ARC-TI Ames Research Center Guided materials discovery combines predictive theory with experimental validation. In production: less than 1 year Optimal choice of ML method Yes No Yes No Yes Yes NAS No N/A

Administration Machine learning methods allow to combine electronic-structure calculations with 

thermodynamic and kinetic modeling; such multi-scale methods are used to predict 

dependence of material properties on composition and structure on multiple scales. The 

composition-structure-property relations are used in materials design: they help to improve 

and optimize properties by compositional adjustment and thermo-mechanical treatment of 

material samples. We provide theoretical guidance for our experimental partners, and we 

develop methods and software for making reliable predictions. 

Machine Learning Airport 

Configuration Prediction

Surface Model: Airport National Aeronautics and Space Ames Research Center The ML-airport-configuration software is developed to provide a reference implementation In production: less than 1 year XGBoost, kedro, scikit-learn, MLFlow, Yes Yes https://github.com/nasa/ML-airport-conf Yes Yes No N/A

Administration to serve as a research example how to train and register Machine Learning (ML) models pipelines

intended for predicting airport configurations. The software is designed to point to 

databases which are not provided as part of the software release and thus this software is 

only intended to serve as an example of best practices. The software is built in python and 

leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software 

provides examples how to build three distinct pipelines for data query and save, data 

engineering, and data science. These pipelines enable scalable, repeatable, and 

maintainable development of ML models.

Machine Learning Airport 

Runway Prediction

Surface Model: Arrival National Aeronautics and Space Ames Research Center The ML-airport-arrival-runway software is developed to provide a reference In production: less than 1 year XGBoost, kedro, scikit-learn, MLFlow, Yes Yes https://github.com/nasa/ML-airport-arrivaYes Yes No N/A

Administration implementation to serve as a research example how to train and register Machine Learning pipelines

(ML) models intended for predicting arrival runway assignments. The software is designed 

to point to databases which are not provided as part of the software release and thus this 

software is only intended to serve as an example of best practices. The software is built in 

python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The 

software provides examples how to build three distinct pipelines for data query and save, 

data engineering, and data science. These pipelines enable scalable, repeatable, and 

maintainable development of ML models.

Machine Learning Airport 

Runway Prediction

Surface Model: Departure National Aeronautics and Space Ames Research Center The ML-airport-departure-runway software is developed to provide a reference In production: less than 1 year XGBoost, kedro, scikit-learn, MLFlow, Yes Yes https://github.com/nasa/ML-airport-depaYes Yes No N/A

Administration implementation to serve as a research example how to train and register Machine Learning pipelines

(ML) models intended for predicting departure runway assignments. The software is 

designed to point to databases which are not provided as part of the software release and 

thus this software is only intended to serve as an example of best practices. The software is 

built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. 

The software provides examples how to build three distinct pipelines for data query and 

save, data engineering, and data science. These pipelines enable scalable, repeatable, and 

maintainable development of ML models.

Machine 

ON Time 

Learning Airport 

Prediction

Surface Model: Estimated National Aeronautics and Space Ames Research Center The ML-airport-estimated-ON software is developed to provide a reference implementation In production: less than 1 year kedro, scikit-learn, MLFlow, pipelines Yes Yes Yes Yes No N/A

Administration to serve as a research example how to train and register Machine Learning (ML) models 

intended for predicting landing time. The software is designed to point to databases which 

are not provided as part of the software release and thus this software is only intended to 

serve as an example of best practices. The software is built in python and leverages opensource libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how 

to build three distinct pipelines for data query and save, data engineering, and data science. 

These pipelines enable scalable, repeatable, and maintainable development of ML models.

GitHub - nasa/ML-airport-estimated-ON:

Machine Learning 

Prediction

Airport Surface Model: Taxi-in National Aeronautics and Space Ames Research Center The ML-airport-taxi-in software is developed to provide a reference implementation to In production: less than 1 year XGBoost, kedro, scikit-learn, MLFlow, Yes Yes https://github.com/nasa/ML-airport-taxi-i Yes Yes No N/A

Administration serve as a research example how to train and register Machine Learning (ML) models pipelines

intended for four distinct use cases: 1) unimpeded AMA taxi in, 2) unimpeded ramp taxi in, 

3) impeded AMA taxi in, and 4) impeded ramp taxi in. The software is designed to point to 

databases which are not provided as part of the software release and thus this software is 

only intended to serve as an example of best practices. The software is built in python and 

leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software 

provides examples of how to build three distinct pipelines for data query and save, data 

engineering, and data science. These pipelines enable scalable, repeatable, and 

maintainable development of ML models.

Machine Learning 

Prediction

Airport Surface Model: Taxi-out National Aeronautics and Space Ames Research Center The ML-airport-taxi-out software is developed to provide a reference implementation to In production: less than 1 year XGBoost, kedro, scikit-learn, MLFlow, Yes Yes https://github.com/nasa/ML-airport-taxi-oYes Yes No N/A

Administration serve as a research example how to train and register Machine Learning (ML) models pipelines

intended for predicting impeded and unimpeded taxi out duration. The software is 

designed to point to databases which are not provided as part of the software release and 

thus this software is only intended to serve as an example of best practices. The software is 

built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. 

The software provides examples how to build three distinct pipelines for data query and 

save, data engineering, and data science for each model. These pipelines enable scalable, 

repeatable, and maintainable development of ML models.

NextGen Advanced Methods: 

Speech2Text and Analysis

ATCSCC Webinar National Aeronautics and Space Federal Aviation Administration Ames Research Center The Advanced Methods project explores the use of innovative and emerging technologies In Production: Less than 1 year Natural Language Processing, Named Entity Yes Agency Generated No N/A Yes No N/A Yes Yes No N/A

Administration to drive post operational analysis of Traffic Management for aircraft. Technologies such as Recognition using Transformer Models, 

machine learning, (ML), artificial intelligence (AI), and advanced data analytics for use in Entity Linking, Unsupervised Learning, 

improving the FAA’s traffic flow management. In this specific use case, our aim is to use Unsupervised clustering using TFdeep learning to convert live ATCSCC webinar meeting conversation to text, and then apply IDF/HDBSCAN/Umap, Trasnformer Model 

natural language processing to the converted text data for later analysis and review. Embeddings, Speech2Text using Deep 

Nerual Network

iguration

l-runway

rture-runway

 The ML-airport-estimated-ON software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival ON time. The software is designed to point to databases which are not provided as part of th

n

ut
NextGen Data Analytics: Letters of Agreement National Aeronautics and Space 

Administration

Federal Aviation Administration Ames Research Center Today, operation constraints are documented via Standard Operating Procedures (SOP) and 

Letters of Agreement (LOA) and are not made available to the public in a consistent manner. 

SOPs are specific to an air traffic control facility and specify the procedures necessary for 

safe operation in the sector. LOA outline agreements establishing procedures and 

responsibilities between two parties (including crossing restrictions, holding patters, 

emergency procedure coordination, etc.) The LOA/SOPs are published internally as scanned 

PDFs and are the responsibility of the facility to maintain. To reduce the manual effort of 

tagging the documents for ease of reference, there is an opportunity to use modern data 

analytics and machine learning to produce and disseminate constraints in a standardized 

manner. 

Providing LOAs or SOPs to stakeholders will enable flight planners (pilots and vendors) to 

study or ingest this information and thereby plan flight trajectories that remain consistent 

with air traffic constraints. It is also fundamental to Next Gen capabilities to share accurate 

data for purposes of creating new noise abatement procedures; improve NAS information 

for common situational awareness and alignment to implement new tools to assist in future 

time-based flow management.

In Production: Less than 1 year Natural Language Processing, Named Entity 

Recognition using Transformer Models, 

Unsupervised Learning, Unsupervised 

clustering using TF-IDF/HDBSCAN/Umap, 

Trasnformer Model Embeddings

YesAgencyGeneratedNoN/AYesNoN/AYes

YesNo N/A

Aero-Engines AI - a machine-learning app 

engine system-performance prediction

for aircraft National Aeronautics and Space Glenn Research Center Aero-Engines AI is a Windows app that deploys machine-learning analytics to predict 

aircraft engine performance. The app was created using Tkinter, a GUI framework that is 

built into the standard Python library. Employing Tkinter greatly facilitates the sharing of 

machine-learning application as an executable file which can be run on Windows machines 

(without the need to have Python or any library installed). Current version of the app 

focuses on the performance prediction of conventional turbofans. The app gets user input 

for a turbofan design, preprocesses the input data, and deploys machine-learning analytics 

to predict turbofan thrust specific fuel consumption (TSFC ), engine weight, engine 

diameter, and core size, respectively. The predictive analytics were built by employing 

supervised deep-learning algorithm to study patterns in an existing open-source database 

of production and research turbofan engines. They were trained, cross-validated, and 

tested in Keras, an open-source neural networks API written in Python, with TensorFlow 

(Google open-source artificial intelligence library) serving as the backend engine. The 

smooth deployment of the machine-learning analytics using the app shows that AeroEngines AI is an easy-to-use and a time-saving tool for aircraft engine design-space 

exploration during the conceptual design stage.

In production: less than 1 year Deep Neural networks, Keras, Python, 

Tensorflow

YesOtherNo Jane's Aero-Engines (available on 

Amazon); FAA TCDS datasheet 

(https://rgl.faa.gov/Regulatory_and_G

uidance_Library/rgMakeModel.nsf/Fra

meset?OpenPage); GRC in-house 

generated data; engine companies 

websites

YesNoYes

YesNo N/A

Administration

Providing visualization tools and streamlining the 

detection and tracking of wildfire-induced smoke 

plumes during the Fire Influence on Regional to 

Global Environments and Air Quality (FIREX-AQ) 

mission

National Aeronautics and Space 

Administration

Jet Propulsion Laboratory Started in FY21 the AI behind the Providing visualization tools and streamlining the 

detection and tracking of wildfire-induced smoke plumes during the Fire Influence on 

Regional to Global Environments and Air Quality (FIREX-AQ) mission is intended to enhance 

efforts in the Earth Science domain. Specifically by providing a hybrid 

unsupervised/supervised data processing pipeline for data fusion and wildfire/smoke 

identification with unique classification products from multiple instruments for further 

structural understanding smoke/fire dynamics.

In production: less than 1 year Restricted Boltzmann Machine (RBM) and 

Conditional Generative Adversarial 

Network (CGAN)

Yes Agency GeneratedYes No

DRAFT Ethical AI ConsiderationsNationalAeronauticsand Space Langley Research Center Not AI, but guidance to inform AI development. Ethical AI considerations in the form of 

draft principles (Fair, Explainable & Transparent, Accountable, Secure & Safe, HumanCentric & Societally Beneficial, Scientifically & Technically Robust). Most draft principles 

were adapted from other organizations' work; the Scientifically & Technically Robust draft 

principle was amplified for NASA because of the Agency's culture and history of strong 

scientific & technical work. The guidance is contributing to multiple NASA discussions and 

debates regarding ethics in AI, to include how existing processes can encourage ethical AI, 

as well as any gaps where new approaches are needed.

In production: less than 1 year Contributing to discussion & debate regarding how 

to approach ethical AI

Ethical AI draft principlesNo N/A

Administration

Parking Lot Advisor National Aeronautics and Space 

Administration

Langley Research Center Select parking lots at NASA Langley Research Center were overcrowded and employees 

needed parking advice to avoid spending time searching for parking. Interns experimented 

with a variety of mechanisms to provide advice on how full a given parking lot was. The end 

solution was one or more cameras strategically positioned to cover the most crowded 

parking lots, with machine learning image recognition used to identify full spaces, empty 

spaces, and give an estimated count of free parking spaces. The prototype worked well but 

the project was put on pause with COVID19 quarantine. Inputs included parking lot images 

and hand labelling of special parking spaces (e.g., handicap, visitor, etc.). Outputs included 

a count of free parking spots. 

In production: less than 1 year This prototype succeeded; however, it was put on 

pause when parking space demand plummeting 

due to quarantine

Image recognition, Recurrent convolutional 

neural networks

Yes Agency GeneratedYesYesNo Code could be made available upon 

project refinement/restart following 

quarantine. However, the imagery data is 

inappropriate to share due to physical 

security concerns.

Yes

Yes This system is running on a firewalled on-premesis server 

in a local data center

NoN/A

Guided Materials Discovery National Aeronautics and Space 

Administration

ARC-TI Ames Research Center Guided materials discovery combines predictive theory with experimental validation. 

Machine learning methods allow to combine electronic-structure calculations with 

thermodynamic and kinetic modeling; such multi-scale methods are used to predict 

dependence of material properties on composition and structure on multiple scales. The 

composition-structure-property relations are used in materials design: they help to improve 

and optimize properties by compositional adjustment and thermo-mechanical treatment of 

material samples. We provide theoretical guidance for our experimental partners, and we 

develop methods and software for making reliable predictions.

Inproduction:lessthan1year

OptimalchoiceofML method

YesNoYesNoYes

YesNASNo

0