Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: AI type = classical_ml; function = image_analysis; monitoring = blank

Generated 2026-10-13 16:39 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: AI type = classical_ml; function = image_analysis; monitoring = blank · 37 records

Key findings

37 use-case records from 5 agencies: 8 deployed (22%), 7 pilots, 22 in development or acquisition, 0 retired, 0 with no stage reported.
2 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
DOI, NASA, USDA report 35 of 37 use cases (95%).
0% of reported deployed use cases (0 of 8) are classified as generative or agentic AI.
Sourcing is reported for 43% of use cases. Where reported, 88% involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	8	22%
Pilot	7	19%
Pre-deployment	22	59%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	37	100%	8

Agencies

Agency	Records	Deployed	High-impact	Completeness
DOI · Department of the Interior	17	3	0	93%
NASA · National Aeronautics and Space Administration	11	0	0	58%
USDA · Department of Agriculture	7	4	2	88%
DOJ · Department of Justice	1	1	0	100%
DHS · Department of Homeland Security	1	0	0	58%

High-impact designation

Designation	Records
High-impact	2
Presumed high-impact, determined not	1
Not high-impact	34

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	2	2
AI impact assessment	2	2
Independent review	2	2
Ongoing monitoring	2	2
Operator training	2	2
Fail-safe	2	2
Appeal process	2	2
User and public consultation	2	2

"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

All 37 records.

Record	Agency ID	Agency	Use case	Stage
R1347	USDA-046	USDA	Rangeland Analysis Platform	Deployed
R1368	USDA-067	USDA	County-Level Remotely-Sensed Corn and Soybean Yield Estimation	Deployed
R1388	USDA-089	USDA	AI for regional forest mapping and monitoring	Deployed
R1390	USDA-091	USDA	Esri ArcGIS Pro Deep Learning Modules	Deployed
R1702	DOJ-0277	DOJ	Object Detection Tool 1	Deployed
R2516	DOI-0247	DOI	Automated Walrus Haulout Monitoring [2024 INV#WO0000000110052]	Deployed
R2533	DOI-0230	DOI	Delineating sub-surface drainage using satellite imagery [2024 INV#WO0000000109525]	Deployed
R2643	DOI-0117	DOI	USGS Flow Photo Explorer [2024 INV#WO0000000109196]	Deployed
R0137	NASA-133	NASA	HyperMapping with Hyperspectral Precise Pointing Optical Sensor (HYPPOS) for Decadal Survey Mission Pathfinder Activities	Pre-deployment
R0152	NASA-189	NASA	Semi-Automatic Landslide Detection (SALaD)	Pre-deployment
R0153	NASA-192	NASA	SpectraClass: Semi-supervised learning in a Jupyter Notebook	Pre-deployment
R0194	NASA-314	NASA	Estimation of Multilayered Cloud Properties from Low-Earth-Orbit Satellite Imager Data	Pre-deployment
R0195	NASA-317	NASA	Geostationary Satellite Sounder Pathfinder Project for 4-D Atmospheric Thermodynamics and Winds	Pre-deployment
R0198	NASA-321	NASA	Improvements in Global Nighttime Satellite Cloud Analyses for Weather and Climate	Pre-deployment
R0214	NASA-360	NASA	Automated Dust detection in satellite imagery	Pre-deployment
R0217	NASA-370	NASA	Marine debris detection using deep learning and high resolution satellite images	Pre-deployment
R0264	NASA-506	NASA	SERVIR Applied Deep Learning Book	Pre-deployment
R0452	NASA-847	NASA	Sub-Saharan West Africa Land Cover Change	Pre-deployment
R0522	NASA-930	NASA	Lunar Foundation Model for Planetary Sciences	Pre-deployment
R1319	USDA-011	USDA	LCAT - Land Change Analysis Technique	Pilot
R1362	USDA-061	USDA	Cover Crop Mapping	Pre-deployment
R1363	USDA-062	USDA	Planting Date Detection	Pre-deployment
R1812	DHS-2667	DHS	Global Maritime Intelligence	Pre-deployment
R2502	DOI-0264	DOI	Remote Sensing Coastal Change - Shoreline Change	Pre-deployment
R2538	DOI-0225	DOI	Automated otolith aging using image processing [2024 INV#WO0000000109315]	Pilot
R2564	DOI-0199	DOI	Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery [2024 INV#WO0000000109078]	Pilot
R2571	DOI-0192	DOI	Machine learning-based landscape feature classification using satellite and airborne imagery [2024 INV#WO0000000108791; 2024 INV#WO0000000108794]	Pilot
R2573	DOI-0190	DOI	Machine Learning Image Classification of Wetlands and Soil moisture [2024 INV#WO0000000108779]	Pilot
R2602	DOI-0157	DOI	Remote sensing of particulate and filter passing mercury species: models and proxies	Pre-deployment
R2605	DOI-0154	DOI	An integrated sensor network and data driven approach to satellite remote sensing of Dissolved Organic Matter	Pre-deployment
R2621	DOI-0138	DOI	Using High-Resolution Imagery and Artificial Intelligence to Support Climate Change Resilience in Agroforestry Across the Pacific	Pre-deployment
R2625	DOI-0137	DOI	Random forest models for predicting water quality of inland waters from remotely sensed imagery	Pilot
R2631	DOI-0125	DOI	MAGMA-net: Multimodal analysis for geophysical monitoring of activity	Pre-deployment
R2646	DOI-0114	DOI	Improving Prediction Capabilities for Barrier Island Landscape Change	Pilot
R2657	DOI-0098	DOI	Forest metrics at Redwood National and State Parks	Pre-deployment
R2701	DOI-0048	DOI	Using Machine Learning to Automate Crack Mapping and Structural Health Monitoring	Pre-deployment
R2714	DOI-0034	DOI	Using advanced computing techniques for image-based monitoring [2024 INV#WO0000000109674]	Pre-deployment

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