Question 1

What are the key details for the project “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery”?

Accepted Answer

The project “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery” is an infrastructure initiative in the Government sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

Question 2

What is the latest status of the project “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery”?

Accepted Answer

The status recorded for “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery” is archived.

Question 3

Who are the main government, EPC, and private stakeholders for “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery”?

Accepted Answer

Taiyo maps the stakeholders of “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery”: government bodies (United States Geological Survey (USGS), United States Department of the Interior (DOI), US Government).

Question 4

What is the budget and commercial profile for the project “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery”?

Accepted Answer

Taiyo normalizes the budget of “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery” to USD 85000.

Question 5

Where can I verify the source and access all details for “Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery”?

Accepted Answer

This Taiyo project page references Grants.gov along with the original URL (https://www.grants.gov/web/grants/view-opportunity.html?oppId=316818). Taiyo standardizes official data into a consistent, searchable project profile.

Sponsoring Agency	Obfuscated Data
Company	Obfuscated Data

Original status	archived
Taiyo status	Obfuscated Data
Taiyo last update	00-00-0000
Available timestamps	00-00-0000
Available timestamp type	Obfuscated Data

Contact name	Obfuscated Data
Phone	0000000000
Email	ObfuscatedData@email.com
Address	Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

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.
Original sub-sector	Obfuscated
Original Currency	USD
Original budget	000000000000000
Procurement method	Obfuscated Data
Budget	000000000000000

Region	Obfuscated
Country	Obfuscated
State	Obfuscated Data
County	Obfuscated
Location	Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Source reliability	High
Data quality score	100%
Source	Obfuscated Data
URL	obfuscated_data,obfuscateddata.com

Project Type	Obfuscated Data
Article Published Date	Obfuscated Data

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

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