Sierra Schuster
Sierra is a Geographer for the Geospatial Sciences and Technologies branch. Her work largely focuses on website development and using machine learning models to detect wildlife in aerial imagery.
Science and Products
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...
Science and Products
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...