Aerial Wildlife Image Repository

Authors

Santhana Krishnan Boopalan, Mississippi State UniversityFollow

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.

Comments

If you are going to use this data in a publication, please cite this paper: https://academic.oup.com/database/article/doi/10.1093/database/baae070/7718812

DOI

https://doi.org/10.54718/WVGF3020

Publication Date

2023

Related Links

• https://projectportal.gri.msstate.edu/awir
• https://academic.oup.com/database/article/doi/10.1093/database/baae070/7718812

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