DOI · DOI-0075 · record R2678

Automating the Detection and Classification of Wildlife in Aerial Imagery [2024 INV#WO0000000109409]

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: 2024-02-21 (day precision)Source: 2/21/2024
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

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.

Expected benefits

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

System outputs

AI outputs will include the location and counts of birds and other wildlife in offshore areas. Imagery, annotations, and code will also be 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 by USFWS and published by USGS through ScienceBase. Expert annotations from New Jersey Audubon biologists published as dataset by USGS.
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