Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: topic = Energy and the Environment; AI type = classical_ml; function = image_analysis

Generated 2026-10-12 07:56 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: topic = Energy and the Environment; AI type = classical_ml; function = image_analysis · 7 records

Key findings

7 use-case records from 3 agencies: 2 deployed (29%), 2 pilots, 3 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 2) are classified as generative or agentic AI.
Sourcing is reported for 57% of use cases. Where reported, 75% involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	2	29%
Pilot	2	29%
Pre-deployment	3	43%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	7	100%	2

Agencies

Agency	Records	Deployed	Completeness
DOI · Department of the Interior	4	0	87%
USDA · Department of Agriculture	2	2	100%
DOE · Department of Energy	1	0	100%

High-impact designation

Designation	Records
Not high-impact	7

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

All 7 records.

Record	Agency ID	Agency	Use case	Stage
R1347	USDA-046	USDA	Rangeland Analysis Platform	Deployed
R1388	USDA-089	USDA	AI for regional forest mapping and monitoring	Deployed
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
R2625	DOI-0137	DOI	Random forest models for predicting water quality of inland waters from remotely sensed imagery	Pilot
R2701	DOI-0048	DOI	Using Machine Learning to Automate Crack Mapping and Structural Health Monitoring	Pre-deployment
R3396	DOE-539	DOE	Drone Imagery Analysis	Pilot

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