Animal Wildlife Population Estimation Using Social Media Images Collections
This addresses the challenge of biodiversity monitoring for conservationists, offering a novel approach to overcome data limitations in traditional methods.
The paper tackles the problem of estimating wildlife population sizes from biased social media images by introducing a new framework that accounts for this bias, showing it is a learnable and potentially solvable issue.
We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches. Here, we present a new framework to take into account the social media bias when using this data source to provide wildlife population size estimates. We show that, surprisingly, this is a learnable and potentially solvable problem.