GRI Publications and Scholarship
Aerial Wildlife Image Repository
Abstract
The availability of an ever-improving repository of datasets allows machine learning algorithms to have a robust training set of images, which in turn allows for accurate detection and classification of wildlife. This repository (AWIR---Aerial Wildlife Image Repository) would be a step in creating a collaborative rich dataset both in terms of taxa of animals and in terms of the sensors used to observe (visible, infrared, Lidar etc.). Initially, priority would be given to wildlife species hazardous to aircrafts, and common wildlife damage-associated species. AWIR dataset is accompanied by a classification benchmarking website showcasing examples of state-of-the-art algorithms recognizing the wildlife in the images.
DOI
https://doi.org/10.54718/WVGF3020
Publication Date
2023
Research Center
Geosystems Research Institute
Recommended Citation
Boopalan, Santhana Krishnan, "Aerial Wildlife Image Repository" (2023). GRI Publications and Scholarship. 2.
https://scholarsjunction.msstate.edu/gri-publications/2