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Matthew Walker (Former Employee)

Science and Products

Non-USGS Publications**

Downey, S.S., Walker, M., Moschler, J. et al. An intermediate level of disturbance with customary agricultural practices increases species diversity in Maya community forests in Belize. Commun Earth Environ 4, 428 (2023). https://doi.org/10.1038/s43247-023-01089-6

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

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An aerial image of a flock of waterfowl floating on the ocean.

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

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
By
Ecosystems Mission Area, Species Management Research Program, Upper Midwest Environmental Sciences Center
link

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

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
Learn More

Science and Products

Non-USGS Publications**

Downey, S.S., Walker, M., Moschler, J. et al. An intermediate level of disturbance with customary agricultural practices increases species diversity in Maya community forests in Belize. Commun Earth Environ 4, 428 (2023). https://doi.org/10.1038/s43247-023-01089-6

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

link
An aerial image of a flock of waterfowl floating on the ocean.

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

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
By
Ecosystems Mission Area, Species Management Research Program, Upper Midwest Environmental Sciences Center
link

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

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
Learn More
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