Remote sensing of particulate and filter passing mercury species: models and proxies

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Energy and the Environment

Operational functions

Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

AI provides the framework for understanding the relationship between optical water quality parameters and non-optical contaminants to develop highly accurate remote sensing models for mercury from satellite images and field measurements

Expected benefits

Synoptic, spatially cohesive maps of mercury distribution in surface water

System outputs

A model that is applied to remote sensing imagery to provide a measurement of mercury and meythlmercury in water bodies

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

Not reported

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

Not reported

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.

Potential impacts and how they were identified

Not reported