Flor-AI: Developing a Remotely Sensed Image Classification Method for Inventory and Monitoring of Flora in Digital UAS Imagery
Description
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.
Detailed example
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.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
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.
Controls / human review
ATO: No; PIA: Not published
Data needed
High-resolution aerial imagery collected with a Wingtra UAS. Expert annotations provided by USFWS biologists.