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  5. Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery

Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery

Dates

Time Period
2024-01-01
Publication Date

Citation

Ke, T., Koneff, M.D., Lubinski, B.R., Robinson, L., Fronczak, D.L., Fara L.J., Landolt, K.L., and White, T.P., 2024, Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery: U.S. Geological Survey data release, https://doi.org/10.5066/P9CBZQV1.

Summary

These data were collected to support the development of detection and classification algorithms to support Bureau of Ocean Energy Management (BOEM) studies and assessments associated with offshore wind energy production. There are 3 child zip files included in this data release. 01_Codebase.zip contains a codebase for using deep learning to filter images based on the probability of any bird occurrence. It includes instructions and files necessary for training, validating, and testing a machine learning detection algorithm. 02_Imagery.zip contains imagery that were collected using a Partenavia P68 fixed-wing airplane using a PhaseOne iXU-R 180 forward motion compensating 80-megapixel digital frame camera with a 70 mm Rodenstock lens. [...]

Contacts

Point of Contact :
Kyle L Landolt
Originator :
Tsung-Wei Ke, Mark D. Koneff, Brian R. Lubinski, Larry R Robinson, David L. Fronczak, Luke J Fara, Kyle L Landolt, Timothy P White
Metadata Contact :
Aaron Murphy
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
SDC Data Owner :
Upper Midwest Environmental Sciences Center
USGS Mission Area :
Ecosystems

Attached Files

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Metadata for Deep Learning Wildlife Detection Model.xml
Original FGDC Metadata
View 		12.16 KB application/fgdc+xml
01_CodeBase.zip 8.3 MB application/zip
17.14 GB application/zip
03_Annotations.zip 4.82 MB application/zip

Purpose

These data were created to complete Step 1 in a proposed 3-stage workflow to support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time. This step involves filtering imagery based on the probability of any bird being present in the image.

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Communities

  • USGS Data Release Products
  • Upper Midwest Environmental Sciences Center (UMESC)

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Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9CBZQV1

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