USDA · USDA-143 · record R1441

Bugnet

Department of Agriculture · Natural Resources and Environment

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Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2023-06-11 (day precision)Source: 6/11/2023
Withheld from public reporting: Not reported

Mission

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

Problem the AI is intended to solve

Detect and map locations of tree damage caused by insects and disease, separate from fire and harvest-caused damage.

Expected benefits

The detection of forest damage using satellite imagery and AI is a safer, faster, and cost-efficient way to monitor forest conditions across entire states and in difficult to reach locations.

System outputs

The outputs predict locations where forest change is likely caused by insect or disease damage as opposed to fire or harvest activity.

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: Yes
Public source code: github.com ↗

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Oregon State University
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: High-resolution satellite imagery; aerial detection surveys; Forest Service Activity Tracking System (FACTS) forest management activity database; Monitoring Trends in Burn Severity (MTBS) fire data.
Federal Data Catalog: Not reported

Governance

0 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 reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported