Wildbook: Crowdsourcing, computer vision, and data science for conservation
Abstract
Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.
Cite
@article{arxiv.1710.08880,
title = {Wildbook: Crowdsourcing, computer vision, and data science for conservation},
author = {Tanya Y. Berger-Wolf and Daniel I. Rubenstein and Charles V. Stewart and Jason A. Holmberg and Jason Parham and Sreejith Menon and Jonathan Crall and Jon Van Oast and Emre Kiciman and Lucas Joppa},
journal= {arXiv preprint arXiv:1710.08880},
year = {2017}
}
Comments
Presented at the Data For Good Exchange 2017