# Wildbook: Crowdsourcing, computer vision, and data science for conservation

arXiv: https://arxiv.org/abs/1710.08880 · HTML: https://mlsanity.com/paper/1710.08880/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 20 of 20)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Species Distribution Modeling for Machine Learning Practitioners: A Review](https://mlsanity.com/paper/2107.10400/) (2021)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [Multispecies Animal Re-ID Using a Large Community-Curated Dataset](https://mlsanity.com/paper/2412.05602/) (2024)
- [BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes](https://mlsanity.com/paper/2411.06896/) (2024)
- [Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources](https://mlsanity.com/paper/2201.10066/) (2022)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation](https://mlsanity.com/paper/2206.02498/) (2022)
- [LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems](https://mlsanity.com/paper/2403.09810/) (2024)
- [Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data](https://mlsanity.com/paper/2304.09657/) (2023)
- [Understanding the Impact of Training Set Size on Animal Re-identification](https://mlsanity.com/paper/2405.15976/) (2024)
- [Improving Wildlife Out-of-Distribution Detection: Africas Big Five](https://mlsanity.com/paper/2506.06719/) (2025)
- [Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning](https://mlsanity.com/paper/2206.02261/) (2022)
- [Nearest-Class Mean and Logits Agreement for Wildlife Open-Set Recognition](https://mlsanity.com/paper/2510.17338/) (2025)
- [The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advances](https://mlsanity.com/paper/2107.14061/) (2021)
- [Navigating an Ocean of Video Data: Deep Learning for Humpback Whale Classification in YouTube Videos](https://mlsanity.com/paper/2212.00822/) (2022)
- [Centering Ecological Goals in Automated Identification of Individual Animals](https://mlsanity.com/paper/2604.20626/) (2026)

## Abstract (arXiv)

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.