NASA is submitting its updated AI Use Case inventory to OSTP for 2023. We followed a 

similar process just as the previous year, soliciting input from the Community of 

Practice of NASA researchers, using a newly deployed web tool. Many of the AI 

activities submitted were in formulation or still in development. This list represents the 

projects where NASA is currently using AI tools developed in-house.

NASA is still developing its RAI guidelines, including generative AI, and we expect those 

to be distributed soon. NASA is implementing EO 13960 directives as follows. To date, 

NASA has published its responsible AI plan, submitted two yearly AI inventories, and 

has fostered a NASA AI Community of Practice where responsible AI is a frequent 

topic. NASA is reviewing and refining an initial Trustworthy AI policy, and has begun 

work on a practical application handbook for practitioners. NASA also participates as a 

regular member of the Responsible AI Council led by the Veterans Administration.
Use Case Name Bureau / Department Summary of Use Case

Stage of System 

Development Life 

Cycle

(Optional) What specific AI 

techniques were used?

(Optional) Where did/does the training 

data originate?

(Optional) Does the 

agency have access to 

the code associated 

with the AI use case?

(Optional) If the source code is 

publicly available, provide link.

(Optional) Is the agency able 

to conduct ongoing testing 

on the code?

(Optional) Is the 

agency able to 

monitor and/or 

audit 

AdaStress Ames Research Center

Testing complex systems often requires computationally intensive Monte Carlo sampling approaches to identify 

possible faults. In systems where the number of faults are low, but safety critical, this form of testing may be 

infeasible due to the large number of samples needed to catch a rare fault. AdaStress instead uses reinforcement 

learning to more efficiently sample low-likelihood, but high-impact faults. In-use Reinforcement Learning Agency Generated Yes Yes Yes

Airplane detection Marshall Space Flight Center Deep learning-based airplane detection from high-resolution satellite imagery In-use Agency Generated Yes

Application that provides bio-inspired solutions 

to engineering problems (PeTaL) Glenn Research Center

PeTaL (the Periodic Table of Life) is an open source artificial intelligence (AI) design tool that leverages data and 

information from nature and technology to advance biomimicry research and development. PeTaL is envisioned to 

streamline various steps of the bio-inspired design process by integrating new and existing tools and methodologies 

around its core ontological framework (Shyam et al., 2019; Unsworth et al., 2019). To be as comprehensive as 

possible, PeTaL requires mass curation of standardized data through which it can learn, interpret, and output 

predictive solutions to design queries. PeTaL is intended to be used by designers and engineers who seek nature’s 

solutions to their design and engineering problems, as well as by biologists who seek to extend the application of 

their scientific discoveries. 

In Production: Classification of biology journal articles into functional categories.

In Development: Joint text summarization and named entity recognition task involving open-access biology journal 

articles using large language models such as those available from OpenAI. In-use

LLM prompt engineering, BERT 

text classification, Natural 

Language Processing Other Yes https://github.com/nasa-petal Yes Yes

ASPEN Mission Planner Jet Propulsion Laboratory

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 Orbitter. In mission

constraint-based heuristic 

search Yes Yes Yes

Automatic Detection of Impervious Surfaces 

from Remotely Sensed Data Using Deep Learning Marshall Space Flight Center

Uses a U-Net based architecture with VGG-19 as an encoder block and custom decoder block to map the impervious 

surfaces using Landsat and OSM data patches In-use Agency Generated Yes

Autonomous Marine Vehicles (Single, Multiple) 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. Numerous 

deployments have been executed including to Monterey Bar (multiple), Chesapeake Bay. In mission

constraint-based heuristic 

search Yes Yes Yes

Autonomous WAiting Room Evaluation (AWARE) Langley Research Center

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. In-use Convolutional Neural Network Yes Yes Yes

Biological and Physical Sciences (BPS) RNA 

Sequencing Benchmark Training Dataset Ames Research Center

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. The implementation uses 

classification methods and hierarchical clustering to identify genes that are predictive of outcomes. In-use GANs, Hierarchical Clustering Agency Generated Yes

https://github.com/NASAIMPACT/bps-numerical Yes Yes

Biological and Physical Sciences Microscopy 

Benchmark Dataset Ames Research Center

This study uses fluorescence microscopy images from the Biological and Physical Sciences Open Science Data 

Repositories (osdr.nasa.gov). The dataset consists of 93,488 images of individual nuclei from mouse fibroblast cells, 

irradiated with Fe particles or X-rays and labeled for DNA double strand breaks using 53BP1 as a fluorescence 

marker. DNA damage appears as small white foci in these images. The study simulates exposure to space radiation 

and the dataset has been modified to be AI ready so that AI expert can test several AI tools on them. The dataset is 

publicly available on the Registry of Open Data on AWS. Implementation AI tools developed in-house are also 

available on the link. In-use Graphical Neural Network Agency Generated Yes

https://github.com/NASAIMPACT/bps-imagery-radiationclassification/tree/cnn_classifier Yes Yes

2023 NASA Use Cases for OSTP May 22 Page 2
CLASP Coverage Planning & Scheduling Jet Propulsion Laboratory

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

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

regions), OCO-3 (atmospheric CO2) and more as well as used for numerous missions analysis and studies (e.g. 

100+). In mission

constraint-based heuristic 

search Yes Yes Yes

Deep Learning Approaches for mapping surface 

water using Sentinel-1 Marshall Space Flight Center Uses a U-Net based architecture to map surface water using the Sentinel-1 SAR Images In-use Agency Generated Yes

Deep Learning-based Hurricane Intensity 

Estimator Marshall Space Flight Center

A web-based situational awareness tool that uses deep learning on satellite images to objectively estimate 

windspeed of a hurricane In-use Agency Generated Yes

Europa Ice Floe Detection (GSFC Planetary 

Sciences Lab) Goddard Space Flight Center

Machine Learning applied to Galileo space probe imagery to detect and classify ice blocks in the chaos regions of 

Jupiter's moon Europa. GANs were also used to generate simulated training data. In-use Mask R-CNN, GANs Agency Generated Yes

https://gitlab.grc.nasa.gov/kgansle

r/europa-ice-floe-detection Yes Yes

Forecasting Algal Blooms With Ai In Lake Atitlán Marshall Space Flight Center

Deep analyses on image datasets from different satellites. Machine learning will help to identify the variables that 

could predict future algal blooms. Knowledge on what those triggers are can turn into precise preventative action, 

not just in Lake Atitlan, but also in other freshwater bodies with similar conditions in Central and South America. In-use Agency Generated Yes

GCMD Keyword Recommender (GKR) Marshall Space Flight Center Natural Language Processing-based science keyword suggestion tool In-use Natural Language Processing Agency Generated Yes

Geophysical Observations Toolkit for Evaluating 

Coral Health (GOTECH) Langley Research Center

Three capstone projects conducted 2021-2022 with Georgia Tech and University of Rochester to develop machine 

learning models that can analyze satellite LIDAR imagery to detect coral reefs and monitor their health. Capstones 

were conducted with support of Coral Vita (an NGO) and the National Institute of Aerospace. Results were 

presented at United Nations COP27. In-use

support vector machine, 

artificial neural network From Another Agency Yes

https://ntrs.nasa.gov/citations/202

20010955 No No

High-Performance Quantum-Classical Hybrid 

Deep Generative Modeling Parameterized by 

Energy-based Models for Flight-Operations 

Anomaly Detection Ames Research Center

Our project conducts high-performance scalable and explainable machine learning for flight-operations 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-use

Convolutional Neural Network, 

#K-Means Clustering, Variational 

Autoencoders Agency Generated Yes Yes Yes

Hybrid On-Board and Ground-Based Processing 

of Massive Sensor Data (HyspIRI IPM) Jet Propulsion Laboratory

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, machine-learned 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 In mission

constraint-based heuristic 

search Yes Yes Yes

ImageLabeler Marshall Space Flight Center Web-based Collaborative Machine Learning Training Data Generation Tool In-use Agency Generated Yes

2023 NASA Use Cases for OSTP May 22 Page 3
Inverse Design of Materials 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. Supervised ML models are 

trained to establish the relationship between how a material is made and how the material performs. Then Bayesian 

optimization is used to select iterative optimal experiments to achieve the target material properties in a cost and 

time efficient manner compared to traditional design of experiments. 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. 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-use

gaussian processes, uncertainty 

quantification, bayesian 

optimization Agency Generated Yes

Data and Code to be made 

available upon project completion Yes Yes

Lessons Learned Bot (LLB) Langley Research Center

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. In-use

Doc2Vec NLP Machine Learning 

Approach Agency Generated Yes Yes Yes

Mapping sugarcane in Thailand using transfer 

learning, a lightweight convolutional neural 

network, NICFI high resolution satellite imagery 

and Google Earth Engine Marshall Space Flight Center

Uses a U-Net based architecture with MobileNetV2 based encoder with transfer learning from global model to map 

the sugarcane pixels in Thailand. This uses NICFI mosaic for the training purpose. In-use Agency Generated Yes

Mexec Onboard Planning and Execution Jet Propulsion Laboratory

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. Specifically, the Europa project is 

concerned with the radiation environment around Jupiter which can trigger on-board computer resets at critical 

times of the mission (e.g. during Europa flybys). If a CPU reset occurs, flight software must bring the spacecraft back 

to a safe state and resume science operations as quickly as possible to minimize science loss. The MEXEC prototype 

flight software was developed to provide such a capability using proven AI planning, scheduling, and execution 

technologies. 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. In-use

constraint-based heuristic 

search Yes Yes Yes

Onboard Planner for Mars2020 Rover 

(Perseverance) Jet Propulsion Laboratory

The M2020 onboard scheduler incrementally constructs a feasible schedule by iterating through activities in priorityfirst 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. Also: 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 multifaceted effort and includes experimentation and demonstrations on-site at JPL's simulated mars navigation yard. In missionconstraint-based heuristic 

search Yes Yes Yes

Pedestrian Safety Corridors for Drone Test Range Langley Research Center

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 onsite 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 humanheavy 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. The results 

have been embedded into the GRASP flight risk simulation tool. In-use Agency Generated Yes

https://gitlab.grc.nasa.gov/dmtren

t/wahldo-1 Yes Yes

Predicting streamflow with deep learning Marshall Space Flight Center

Uses a long short-term memory model to predict streamflow at USGS gauges sites with inputs from the NASA Land 

Information System and forecasts of precipitation In-use Agency Generated Yes

Prediction of Mass Level in Radio Frequency 

Cryogenics Ames Research Center

Utilizing the Radio frequency signature of fluids in a tank, the ML model predicts the level of fluid in the tank. In 

micro-gravity standard fluid level detection methods do not work because the fluid is not restricted to any shape or 

definition. In-use

Deep Neural Network, Temporal 

Convolutional Network Agency Generated Yes Yes Yes

2023 NASA Use Cases for OSTP May 22 Page 4
Pre-trained microscopy image neural network 

encoders Ames Research Center

Convolutional Neural Network encoders were trained on over 100,000 microscopy images of materials. 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. Current work is ongoing to deploy the encoders for 

generative tasks and 3D texture synthesis tasks. The technology has been used to quantify the microstructure of 

numerous materials including SLS core stage welds, Ni-based superalloys, composites, and oxide dispersion 

strengthened alloys. Establishing the relationship between processing (how a material is made), microstructure (the 

atomisitc and phase arrangement of a material), and properties of materials is fundemental to the design and 

development of new materials. Microstructure is often analyzed qualitatively or by tedious manual measurements. 

This technology enables and improves the rapid quantification of material microstructure from microscope images 

for use in data-driven approaches to design materials faster. In-use Transfer learning, Agency Generated Yes

https://github.com/nasa/pretraine

d-microscopy-models Yes Yes

SensorWeb: Volcano, Flood, Wildfire, and 

others. Jet Propulsion Laboratory

The Sensor Web Project uses a network of sensors linked by software and the internet to an autonomous satellite 

observation response capability. 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.) In mission

constraint-based heuristic 

search Yes Yes Yes

Ship detection Marshall Space Flight Center Deep learning-based ship detection from high-resolution satellite imagery In-use Agency Generated Yes

Similarity Search for Earth Science Image Archive Marshall Space Flight Center Self Supervised Based Learning approach to search image archives using a query image In-use Agency Generated Yes

Titan Methane Cloud Detection (GSFC Planetary 

Sciences Lab) Goddard Space Flight Center

Machine Learning applied to Cassini space probe imagery to detect and characterize methane clouds on Saturn's 

moon Titan. In-use

Mask R-CNN, U-net image 

recognition Agency Generated Yes

https://gitlab.grc.nasa.gov/zyahn/ti

tan-clouds-project Yes Yes

TRN (Terrain Relative Navigation) Jet Propulsion Laboratory

Terrain Relative Navigation (TRN) estimates position during Mars landing by automatically matching landmarks 

identified in descent images to a map generated from orbital imagery. 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 and will be used on Mars Sample Return Lander. In-use

Computer vision and state 

estimation. No

2023 NASA Use Cases for OSTP May 22 Page 5