mlsanityData to 14 Oct 2026

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

arXiv 1710.08880 ↗First posted 24 Oct 2017cs.CYcited by 20

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 2; 2024: 4; 2025: 2; 2026: 1.

20 papers.

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  1. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 130
  2. 2021Perspectives on individual animal identification from biology and computer visioncited by 31
  3. 2022The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Countingcited by 24
  4. 2022SealID: Saimaa ringed seal re-identification datasetcited by 22
  5. 2021Species Distribution Modeling for Machine Learning Practitioners: A Reviewcited by 21
  6. 2023The Impact of Imperfect XAI on Human-AI Decision-Makingcited by 21
  7. 2024Multispecies Animal Re-ID Using a Large Community-Curated Datasetcited by 18
  8. 2024BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopescited by 14
  9. 2022Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resourcescited by 11
  10. 2021ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identificationcited by 8
  11. 2022NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregationcited by 5
  12. 2024LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systemscited by 4
8 more
  1. 2023Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Datacited by 2
  2. 2024Understanding the Impact of Training Set Size on Animal Re-identificationcited by 2
  3. 2025Improving Wildlife Out-of-Distribution Detection: Africas Big Fivecited by 2
  4. 2022Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learningcited by 1
  5. 2025Nearest-Class Mean and Logits Agreement for Wildlife Open-Set Recognitioncited by 1
  6. 2021The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advancescited by 0
  7. 2022Navigating an Ocean of Video Data: Deep Learning for Humpback Whale Classification in YouTube Videoscited by 0
  8. 2026Centering Ecological Goals in Automated Identification of Individual Animalscited by 0