Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia.

ABSTRACT: Assessing the numbers and distribution of at-risk megafauna such as the black rhino (Diceros bicornis) is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Satellite monitoring is emerging as a potential tool for very large animals (e.g., elephant) but detecting smaller species requires higher resolution imaging. Drones can deliver the required resolution and speed of monitoring, but challenges remain in delivering automated monitoring systems where internet connectivity is unreliable or absent. This study describes a model built to run on a drone to identify in situ images of megafauna. Compared with previously reported studies, this automated detection framework

SUBMITTER: Hua A 

PROVIDER: S-EPMC9356584 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature