Location-specific machine learning models trained on synthetic images represent a scalable paradigm for quantifying biodiversity. Digital images of specimens or samples can be manipulated and combined with computer-generated scene elements to produce nearly infinite synthetic images for training machine learning models. Application of this method for the automatic cataloging of wildlife from camera trap images is of urgent need during this time of high extinction rate and environmental change.
NVSS hatching prediction poster
Events and Conferences
Creating and Using Synthetic Data Sets to Train Machine Learning Models For Species Level Identification of Herpetofauna In-Situ
Ecological Society of America Conference, Montreal, Canada
Seth Frazer, Christopher J. Evelyn, Constance J. Woodman
American Ornithological Society & Birds Carribean Ornithological conference
San Juan, Puerto Rico
Woodman, C. J. 2022, June 27 – 2 July. USDA open-source technology: Smart camera traps for nests [Conference presentation]. American Ornithological Society & Birds Carribean 2022 Ornithological Conference, San Juan, Puerto Rico.
National Veterinary Scholars Symposium 2022
University of Minnesota
Woodman, C. J.