Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The summary for the Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Geological Survey, which is the U.S. government agency offering this grant.

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery: The Bureau of Ocean Energy Management (BOEM), and the US Fish and Wildlife Service (USFWS) Division of Migratory Bird Management (DMBM), Branch of Migratory Bird Surveys, and US Geological Survey (USGS) are funding and collaborating on studies to develop deep learning algorithms that automate the process of detecting and classifying waterfowl, seabirds, and other marine wildlife species. BOEM has prioritized the use of Outer Continental Shelf Program funds by USGS in FY19, FY20, and FY21 to advance development of an imagery and annotation database and development of deep learning algorithms (DLA) (https://www.boem.gov/Environmental-Stewardship/Environmental-Studies/Partnerships/Partner-USGS.aspx). This project will advance the application of computer vision and deep learning methods to automated detection and classification of waterfowl, seabirds, and other marine wildlife species from digital aerial imagery.