DOI · DOI-0079 · record R2677

Flor-AI: Developing a Remotely Sensed Image Classification Method for Inventory and Monitoring of Flora in Digital UAS Imagery

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: 2024-11-01 (day precision)Source: 11/1/2024
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

This project supports the management of oak-pine barrens on the Necedah National Wildlife Refuge, Wisconsin. Necedah NWR staff conduct habitat management actions (prescribed burning, mowing, seeding, and herbicide application) to increase wild lupine (Lupinus perennis) abundance for the endangered Karner blue butterfly (Lycaeides melissa samuelis). This project will use uncrewed aerial systems (UAS) to collect imagery and apply artificial intelligence to efficiently process imagery and detect lupine. Model performance and the overall project workflow will inform future efforts and may be applied to additional vegetation monitoring programs.

Expected benefits

Benefits of using UAS and AI for this work include more efficient surveys of large landscapes, reduced costs associated with staffing for on-the-ground surveys, and faster generation of maps for planning management actions.

System outputs

AI outputs will include the location, counts, and coverage area of wild lupine populations in the target areas. Imagery will also be processed into orthomosaics and published.

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: 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: High-resolution aerial imagery collected with a Wingtra UAS. Expert annotations provided by USFWS biologists.
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