Automating the Detection and Classification of Wildlife in Aerial Imagery [2024 INV#WO0000000109409]
Description
The tools and workflows developed by this project will be used by the Bureau of Ocean Energy Management (BOEM) to assess wildlife populations as part of planning and monitoring for offshore energy development. BOEM requires information on the environmental sensitivity of marine species and marine productivity to make informed decisions regarding offshore oil and gas energy infrastructure placement and mitigation measures. This is mandated by the Outer Continental Shelf Lands Act (OCSLA Section 18(2)(G)), which requires the Secretary of the Interior to consider these factors when determining the size, timing, and location of future lease sales for the National Oil and Gas Leasing Program.
Detailed example
AI outputs will include the location and counts of birds and other wildlife in offshore areas. Imagery, annotations, and code will also be 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
This work will support BOEM's activities to manage resources within the OCS Planning Areas, informing decisions about lease area selection and mitigation strategies. Using AI will reduce the time, cost, and risk associated with aerial surveys
Controls / human review
ATO: No; PIA: Not published
Data needed
High-resolution aerial imagery collected by USFWS and published by USGS through ScienceBase. Expert annotations from New Jersey Audubon biologists published as dataset by USGS.