Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Improving Animal Monitoring Using Small Unmanned Aircraft Systems (Suas) And Deep Learning Networks, Meilun Zhou, Jared A. Elmore, Sathishkumar Samiappan, Kristine O. Evans, Morgan Pfeiffer, Bradley F. Blackwell, Raymond B. Iglay
Improving Animal Monitoring Using Small Unmanned Aircraft Systems (Suas) And Deep Learning Networks, Meilun Zhou, Jared A. Elmore, Sathishkumar Samiappan, Kristine O. Evans, Morgan Pfeiffer, Bradley F. Blackwell, Raymond B. Iglay
USDA Wildlife Services: Staff Publications
In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with …
Estimating Wildlife Strike Costs At Us Airports: A Machine Learning Approach, Levi Altringer, Jordan Navin, Michael J. Begier, Stephanie A. Shwiff, Aaron M. Anderson
Estimating Wildlife Strike Costs At Us Airports: A Machine Learning Approach, Levi Altringer, Jordan Navin, Michael J. Begier, Stephanie A. Shwiff, Aaron M. Anderson
USDA Wildlife Services: Staff Publications
Current lower bound estimates of the economic burden of wildlife strikes make use of mean cost assignment to impute missing values in the National Wildlife Strike Database (NWSD). The accuracy of these estimates, however, are undermined by the skewed nature of reported cost data and fail to account for differences in observed strike characteristics—e.g., type of aircraft, size of aircraft, type of damage, size of animal struck, etc. This paper makes use of modern machine learning techniques to provide a more accurate measure of the strike-related costs that accrue to the US civil aviation industry. We estimate that wildlife strikes …