DOI · DOI-0069 · record R2684

LANDFIRE

Department of the Interior · USGS

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2025-01-01 (day precision)Source: 1/1/2025
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

USGS EROS has provided the expertise and staff to conduct LANDFIRE mapping for over 20 years. LANDFIRE products provide the nationally consistent, high-quality vegetation and fuels data needed to support wildfire management, land-use planning, ecosystem restoration, and habitat management. By producing and updating these datasets, EROS ensures that federal, state, tribal, and local partners all work from the same trusted information, improving coordination and decision-making. The national scope and scientific rigor of EROS mapping make LANDFIRE an indispensable foundation for assessing risk, allocating resources, and sustaining resilient landscapes across the United States.

Expected benefits

LANDFIRE produces a variety of geospatial products to support the fire community, including Existing Vegetation Cover, Existing Vegetation Type, Existing Vegetation Height, Environmental Site Potential, Biophysical Settings, and more

System outputs

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: Landsat data are the foundation of LANDFIRE data products. Field plot to train and validate mapping and monitoring datasets include data from the Forest Inventory and Analysis (FIA) program, Natural Resources Conservation Service (NRCS), National Park Service, and other federal, state, and academic sources.
Federal Data Catalog: www.landfire.gov ↗

Governance

5 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported