Federal AI Intelligence · U.S. Government AI Observatory

Federal AI landscape: executive briefing

Filtered: agency = NASA; topic = Science; AI type = classical_ml

Generated 2026-10-13 04:06 UTCDataset: 2025 Federal Agency AI Use Case Inventory (individually reported use cases), Office of Management and Budget (OMB). Version 2025-inventory@62d5baa65fa3, processed 2026-10-11, rules 2026.10.1. Static snapshot.Applied filters: agency = NASA; topic = Science; AI type = classical_ml · 263 records

Key findings

263 use-case records from 1 agencies: 11 deployed (4%), 10 pilots, 242 in development or acquisition, 0 retired, 0 with no stage reported.
0 records are designated high-impact; 0 of them (n/a) answered at least one minimum-practice question.
0% of reported deployed use cases (0 of 11) are classified as generative or agentic AI.
Sourcing is reported for 8% of use cases. Where reported, 100% involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	11	4%
Pilot	10	4%
Pre-deployment	242	92%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	263	100%	11

Agencies

Agency	Records	Deployed	High-impact	Completeness
NASA · National Aeronautics and Space Administration	263	11	0	61%

High-impact designation

Designation	Records
Presumed high-impact, determined not	1
Not high-impact	262

Governance evidence: use cases designated high-impact

Practice	In place	In progress	Not in place	N/A or precluded	Other	No answer	In scope
Pre-deployment testing	0	0	0	0	0	0	0
AI impact assessment	0	0	0	0	0	0	0
Independent review	0	0	0	0	0	0	0
Ongoing monitoring	0	0	0	0	0	0	0
Operator training	0	0	0	0	0	0	0
Fail-safe	0	0	0	0	0	0	0
Appeal process	0	0	0	0	0	0	0
User and public consultation	0	0	0	0	0	0	0

"In place" means the agency reported the practice as established. "No answer" means the field was blank; it says nothing about whether the practice exists.

Method

All figures are computed directly from the normalised inventory records. Category labels are normalised from the source answer options; original values are kept on each record. Governance answers are classified as in place, in progress, not in place, not applicable, precluded, waived, other or no answer, and blanks are never read as "no". See the Methodology page for full rules.

Limitations

Figures are what agencies reported in the 2025 inventory. They are not verified, and registration of use cases is not complete for every agency.
A blank answer is missing evidence, not evidence that a practice or safeguard is absent.
Counts are use-case records. A larger portfolio does not indicate better performance or greater AI maturity, and agencies differ in how they split systems into use cases.
The inventory has no budget, cost, accuracy or outcome data. None is shown or inferred here.
This is a disclosure analysis. It is not an audit, a compliance determination or an official government assessment.

Source records

First 40 of 263 records, deployed first. The full list is available as CSV from the explorer with the same filters.

Record	Agency ID	Agency	Use case	Stage
R0161	NASA-214	NASA	AEGIS: Autonomous Exploration for Gathering Increased Science	Deployed
R0164	NASA-221	NASA	CLASP Coverage Planning & Scheduling	Deployed
R0166	NASA-225	NASA	Enhanced AutoNav for Perseverance Rover on Mars	Deployed
R0170	NASA-234	NASA	MLNav (Machine Learning Navigation)	Deployed
R0172	NASA-237	NASA	Perseverance Rover on Mars - Terrain Relative Navigation	Deployed
R0249	NASA-451	NASA	SensorWeb: Volcano, Flood, Wildfire, and others.	Deployed
R0393	NASA-708	NASA	Global, Seasonal Mars Frost Maps	Deployed
R0453	NASA-848	NASA	Near-real-time aerosol retrievals from OMPS Limb Profiler measurements	Deployed
R0454	NASA-849	NASA	Retrieving stratospheric water vapor from OMPS Limb Profiler measurements	Deployed
R0458	NASA-854	NASA	Application of ML to Detection of Anomalies in Spacecraft Health and Status Data	Deployed
R0520	NASA-928	NASA	Dirty Vacuum rated 6-Axis Robotic Arm Toolpathing	Deployed
R0104	NASA-1	NASA	Anomaly Detection and Precursor Identification in UAV flight data	Pre-deployment
R0108	NASA-10	NASA	ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes	Pre-deployment
R0110	NASA-13	NASA	Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model	Pilot
R0114	NASA-21	NASA	Machine Learning Airport Surface Model: Estimated ON Time Prediction	Pre-deployment
R0115	NASA-22	NASA	Machine Learning Airport Surface Model: Taxi-in Prediction	Pre-deployment
R0120	NASA-35	NASA	Aero-Engines AI - a machine-learning app for aircraft engine system-performance prediction	Pre-deployment
R0121	NASA-40	NASA	Graph Neural Networks for Airfoil Performance Prediction	Pre-deployment
R0122	NASA-41	NASA	Inverse Design of Materials	Pre-deployment
R0124	NASA-64	NASA	Applying machine learning techniques to enhance the sensitivity and selectivity of multifunctional sensor platform	Pre-deployment
R0125	NASA-66	NASA	Artificial Intelligent Co-Processor Slice with Google Coral TPU For Automated Onboard Data-Product Generation	Pre-deployment
R0128	NASA-93	NASA	Deep Learning for Communication-Limited Spacecraft	Pre-deployment
R0129	NASA-97	NASA	Development of new tools for detecting and assessing resilient agriculural systems farm performance	Pre-deployment
R0130	NASA-100	NASA	Earth Information System Fire Pilot	Pre-deployment
R0131	NASA-105	NASA	Enabling Autonomous Differential Drag Control and Attitude Maneuvering Using Onboard Artificial Intelligence (AI) for SmallSat Distributed Space Missions (DSMs)	Pre-deployment
R0132	NASA-113	NASA	Forest responses to climate change	Pre-deployment
R0133	NASA-123	NASA	From Single Spacecraft to Synchronized Swarms: Fault Diagnosis for Distributed Spacecraft Mission Resilience	Pre-deployment
R0134	NASA-127	NASA	HEASARC X-ray Spectra and Light Curve data sets	Pre-deployment
R0135	NASA-128	NASA	High Rate Digital Spectrometer Enhancement with Neural Network AI	Pre-deployment
R0136	NASA-129	NASA	High Resolution Earth and Planetary Atmospheric Predictions using Machine Learning	Pre-deployment
R0137	NASA-133	NASA	HyperMapping with Hyperspectral Precise Pointing Optical Sensor (HYPPOS) for Decadal Survey Mission Pathfinder Activities	Pre-deployment
R0139	NASA-148	NASA	M4OPT: Multi-Mission Multi-Messenger Observation Planning Toolkit	Pre-deployment
R0140	NASA-149	NASA	Machine Intelligence for Small Satellites	Pre-deployment
R0141	NASA-154	NASA	Machine Learning of Ocean Worlds Laboratory Analog Seawater Volatiles	Pre-deployment
R0142	NASA-161	NASA	MERRAMax Automated feature selection	Pre-deployment
R0143	NASA-166	NASA	Multi-temporal analytic center framework	Pre-deployment
R0144	NASA-169	NASA	Optimization of Machine Learning Algorithms for Lidar to Detect Aerosols, Clouds, and the PBL (Planetary Boundary Layer)	Pre-deployment
R0145	NASA-171	NASA	Parameter estimation, emulator building, model tuning and sensitivity analysis of the ocean carbon cycle using ML	Pre-deployment
R0146	NASA-172	NASA	PIC-ASIC Spectrometer Demonstration for Ultrawideband and Hyperspectral Microwave Sounding	Pre-deployment
R0148	NASA-182	NASA	RAMjET: RApid Machine lEarned Triage - AI to classify astrophysical phenomena in photometric lightcurves	Pre-deployment

Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory