DOI · DOI-0088 · record R2665
Reducing elevation error in coastal wetland digital elevation models
Department of the Interior · USGS
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
- 2022-10-04 (day precision)Source: 10/4/2022
- 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
The objective of this use case was to train/deploy a random forest regression model to reduce elevation error in a coastal wetland digital elevation model.
Expected benefits
The model led to an enhanced digital elevation model for coastal wetland areas.
System outputs
Prediction
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
- In situ elevation data was collected and used for the training and testing data development. The predictor variables used in the model included elevation data and satellite imagery.
- Federal Data Catalog
- doi.org ↗
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