USDA · USDA-021 · record R1326
Forest Health Detection Monitoring
Department of Agriculture · Natural Resources and Environment
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
- 2021-06-29 (day precision)Source: 6/29/2021
- 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
This project monitors forest health by detecting tree damage through changes in light patterns collected by satellites.
Expected benefits
This detection method helps the Forest Health Protection program monitor forested areas that can't be checked on the ground or with aerial surveys.
System outputs
The model predicts damaged areas based on image inputs.
Technology
- AI classification
- Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
- System name(s)
- Not reported
- Custom-developed code
- Yes
- Public source code
- Not reported
Sourcing
- How it was built
- Contract and in-houseSource: c) Developed with both contracting and in-house resources
- Vendor (as reported)
- RedCastle Resources
- 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
- Training data was collected from the National Agriculture Imagery Program (NAIP) and WorldView (vendor is Maxar) imagery. Similar high resolution imagery has been used in evaluation and validation. There are approximately 1000 labeled images in the training dataset, each containing spectral 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