DOI · DOI-0072 · record R2681
Machine learning in remote sensing-based wildfire and natural resource risk assessments
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
- 2024-11-01 (day precision)Source: 11/1/2024
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Emergency Management
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
Predict the risks of wildfire, drought, and invasive species spread on assets of value to the American public
Expected benefits
Predictions inform land management planning and decision-making to mitigate risk and save American taxpayers millions of dollars in damage from the additive effect of these stressors.
System outputs
Maps of risk, prediction of which factors contribute to risk, highlight areas for potential mitigation actions.
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
- Publicly available satellite imagery, wildfire perimeters and burn severity, topographic information, drought indices
- Federal Data Catalog
- Not reported
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