Closed · USGS-19-FA-0203 · CFDA 15.808 · Discretionary
Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery
Federal grant opportunity posted by Geological Survey, cataloged on Grants.gov.
- $85K-$85K
- Award range
- Closed
- Status
- June 17, 2019
- Close date
- 1
- Expected awards
The verdict
Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery is a closed discretionary listing from Geological Survey that offered $85,000 -- $85,000 across 1 expected award. Future funding cycles may be published under the same CFDA number. PlainGrants indexes 2,522 other opportunities from Geological Survey.
- $85K-$85K
- award range
- Closed
- application status
- 1
- expected award
- 15.808
- CFDA program
Archived opportunity record
Deep Learning for Automated Detection and Cla… is closed (last close June 17, 2019). Check the agency catalog for replacement NOFOs. Geological Survey still indexes 2,522 other opportunities here.
According to Grants.gov (extract vintage 2026-08-10), this record reflects the officially published listing. See methodology.
Deadline tracker
Application window closed
Closed June 17, 2019
Est. Total Funding
$85,000
Expected Awards
1
Instrument
Cooperative Agreement
Description
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.
Campus applicant desk · Natural Resources
Grants.gov prints only higher-education applicant doors for Deep Learning for Automated Detection and Cla… (Public / state-controlled institutions of higher education). This reads as a campus competition desk, not a civic or commercial open call. The extract does not mark applicant cost-sharing as required. Instrument: Cooperative Agreement. Funding-activity code maps to Natural Resources (NR). 317 other Geological Survey listings share this eligibility-code string (13% of 2,523).
- Code 06
- Public / state-controlled institutions of higher education
Codes are Grants.gov eligibility category values from the extract vintage 2026-08-10. Confirm the full eligibility statement on the official listing or the Grants.gov eligibility reference.
Official Listing on Grants.gov
View full details, application forms, and submission instructions.
Agency Contact
Desiree T Santa Grant Specialist Phone 703-648-7382
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What to do next
Deep Learning for Automated Detection and Cla… is closed; use the parent program and agency catalog for the next funding cycle.
- Browse open federal opportunities in the same category when this listing is closed. Open opportunities
- Look up CFDA 15.808 across all indexed listings before you commit to this opportunity alone. Eligibility finder
- Compare award ranges across federal categories before you pick a single CFDA. All categories
Opportunity deadlines and eligibility change on Grants.gov; PlainGrants mirrors the extract and is not the submission system.
Disclaimer: This information is sourced from Grants.gov and SAM.gov and is for informational purposes only. Opportunity details, deadlines, and eligibility requirements change frequently. Always verify current information directly on Grants.gov before applying. PlainGrants is not affiliated with any federal agency.
Read our methodology - how this data is sourced, computed, and verified.
| Publisher | PlainGrants |
| Sources | the SAM.gov Assistance Listings and Grants.gov |