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.
Education and Certifications
B.S. in Computer Science, Western Washington University at Bellingham, 2023
Science and Products
Riverine Acoustic Fish Telemetry (RAFT) Network
The Riverine Acoustic Fish Telemetry (RAFT™) Network is a node of the Ocean Tracking Network (OTN) and serves as a repository to access and archive acoustic fish telemetry data from projects occurring across the Mississippi River Basin. By sharing the locations of receivers and detections of member transmitters across all receivers in the Network, RAFT provides an avenue for collaboration and...
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...
Aerial imagery from the SEABirD (Safe, Efficient, Aerial Bird Detection) Platform collected over the Atlantic Ocean Aerial imagery from the SEABirD (Safe, Efficient, Aerial Bird Detection) Platform collected over the Atlantic Ocean
The U.S. Fish and Wildlife Service (USFWS) collected aerial imagery over the Atlantic Ocean using the Safe, Efficient, Aerial Bird Detection (SEABirD) platform. These images are taken over the Atlantic Ocean using a seven-camera array optimized for high resolution imagery. Published imagery are 3-band (RGB) 8-bit JPG files and are not georeferenced or orthorectified. Each image contains...
Science and Products
Riverine Acoustic Fish Telemetry (RAFT) Network
The Riverine Acoustic Fish Telemetry (RAFT™) Network is a node of the Ocean Tracking Network (OTN) and serves as a repository to access and archive acoustic fish telemetry data from projects occurring across the Mississippi River Basin. By sharing the locations of receivers and detections of member transmitters across all receivers in the Network, RAFT provides an avenue for collaboration and...
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...
Aerial imagery from the SEABirD (Safe, Efficient, Aerial Bird Detection) Platform collected over the Atlantic Ocean Aerial imagery from the SEABirD (Safe, Efficient, Aerial Bird Detection) Platform collected over the Atlantic Ocean
The U.S. Fish and Wildlife Service (USFWS) collected aerial imagery over the Atlantic Ocean using the Safe, Efficient, Aerial Bird Detection (SEABirD) platform. These images are taken over the Atlantic Ocean using a seven-camera array optimized for high resolution imagery. Published imagery are 3-band (RGB) 8-bit JPG files and are not georeferenced or orthorectified. Each image contains...