Wildbook in 60 Seconds

Wildbook blends structured wildlife research with artificial intelligence, citizen science, and computer vision to speed population analysis and develop new insights to help fight extinction. Here is our vision in one minute.

News

2017-09-03

A new paper using data from Wildbook for Whale Sharks was published!

Reynolds SD, Norman BM, Beger M, Franklin CE, Dwyer RG. Movement, distribution and marine reserve use by an endangered migratory giant. Divers Distrib. 2017;00:1–12.
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2017-08-16

Check out the newest Wildbook: Giraffespotter. Giraffespotter might be our best looking Wildbook yet and represents a consortium of collaborative researchers studying endangered giraffes in Africa.

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Why Wildbook?

According to a July 2017 study in the Proceedings of the National Academy of Sciences, a “sixth mass extinction” is underway, a trend signalled by widespread vertebrate losses that “will have negative cascading consequences on ecosystem functioning and services vital to sustaining civilization.” This meta-study is based on multiple, independent analyses and represents a growing awareness in the wildlife research community that more rapid assessment, response, and review are needed to understand and counter this decline.

Unfortunately, wildlife research efforts are frequently underfunded and small scale. The collection and management of wildlife data remains a largely ad hoc and academic exercise focused on moving small data sets (often in Excel and Access) into local, custom population studies for “one-off” analyses without long-term data curation or collaboration across borders and regions. Arriving at a critical mass of data for population analysis can take years (especially for rare or endangered species). Long required observation periods and manual data processing (e.g., matching photos “by eye”) can create multi-year lags between study initialization and scientific results, as well as create conclusions too coarse or slow for effective and optimizable conservation action. This limits the scope, scale, repeatability, continuity, and ROI of the studies as they face the limits of their home-grown tools and IT capabilities.

Wildlife researchers lack a common yet customizable platform for collaboration and often don’t have the technical experience or budget to take advantage of advanced computing tools (e.g., computer vision, artificial intelligence). These tools allows projects to obtain, curate, and analyze “Big Data”, such as the potential of citizen scientists to collect and contribute large volumes of wildlife data through tourism and volunteerism.

Data Management in the Cloud

Wildbook ® is an open source software framework to support collaborative mark-recapture, molecular ecology, and social ecology studies, especially where citizen science and artificial intelligence can help scale up projects. It is developed by the non-profit Wild Me (PI Jason Holmberg) and research partners at the University of Illinois-Chicago (PI Tanya Berger-Wolf), Rensselaer Polytechnic Institute (PI Charles V. Stewart), and Princeton University (PI Daniel Rubenstein).

Wildbook provides a technical foundation (database, APIs, computer vision, etc.) for wildlife research projects that are:

  • tracking individual animals in a wildlife population using natural markings , genetic identifiers, or vocalizations
  • collecting biological samples from a wildlife population and performing genetic and/or chemical analyses (e.g., stable isotope measurements, haplotype determination, etc.)
  • engaging citizen scientists and\or using social media to collect sighting information
  • looking to build a collaborative, distributed research network for a migratory and/or global species
  • looking to develop a new animal biometrics solution (e.g., pattern matching from photos) for one or more species
  • collecting behavioral and/or social data for a wildlife study population

Wildbook Ecosystem of wildlife, people, IT infrastructure, and intelligent agents.

The biological and statistical communities already support a number of excellent tools, such as Program MARK,GenAlEx, and SOCPROG for use in analyzing wildlife data. Wildbook is a complementary software application that:

  • provides a scalable and collaborative platform for intelligent wildlife data storage and management, including advanced, consolidated searching
  • provides an easy-to-use software suite of functionality that can be extended to meet the needs of wildlife projects, especially where individual identification is used
  • provides an API to support the easy export of data to cross-disciplinary analysis applications (e.g., GenePop ) and other software (e.g., Google Earth)
  • provides a platform that supports the exposure of data in biodiversity databases (e.g., GBIF and OBIS)
  • provides a platform for animal biometrics that supports easy data access and facilitates matching application deployment for multiple species

Gateway to A.I. and Computer Vision

Images have become the most abundant, available and cheap source of data. The explosive growth in the use of digital cameras, together with rapid innovations in storage technology and automatic image analysis software, makes this vision possible particularly for large animals with distinctive striped, spotted, wrinkled or notched markings, such as elephants, giraffes and zebras. This large number of collected images must be analyzed automatically to produce a database that records who the animals are, where they are, and when they were photographed. Combining this with geographic, environmental, behavioral and climate data would enable the determination of what the animals are doing, and why they are doing it.

Wildbook evolved out of multi-disciplinary, collaborative research conducted under National Science Foundation support (see ibeis.org). Wildbook employees computer vision and A.I. components to detect features in submitted images and detect and then identify individual animals. Wildbook brings massive-scale computer vision to wildlife research for the first time.

Wildbook integrates the data management software of Wild Me with the computer vision and A.I. research of RPI. Wildbook includes a two-part, multi-species computer vision pipeline to find and identify individual animals in photos collected under real-world conditions, especially with citizen science contribution.

Detection

Our detection pipeline is a cascade of deep convolutional neural networks (DCNNs) that applies a fully-connected classifier on extracted features. Three separate networks produce: (1) whole-scene classifications looking for specific species of animals in the photograph, (2) object annotation bounding box localizations, and (3) the viewpoint, quality, and final species classifications for the candidate bounding boxes.

In Wildbook, A.I.-powered detection finds and labels wildlife in photos.

Identification

The second major computer vision step is identification, which assigns a name label to each annotation from detection. To do this, SIFT descriptors are first extracted and then compared at keypoint locations. Scores from the query that match the same individual are accumulated to produce a single potential score for each animal. The animals in the database are then ranked by their accumulated scores. A post-processing step spatially verifies the descriptor matches and then re-scores and re-ranks the database individuals.

Example correct identifications. The upper left annotation in each frame is the annotation to be identified. The other frames are the other annotations for the same animal. The bottom left annotation is the primary matching frame. The colored line segments show connections between corresponding features of the same animal.

Analysis

The results of computer vision are returned to Wildbook’s data management software, which supports rapid curation, export, and analysis. Data can be rapidly viewed in tables, maps, charts, calendars, and as thumbnails. Data can also be searched, filtered, and used in R, Mark, ArcGIS, Google Earth, and other applications.

Projects with Wildbook

Wildbook is used in public and private installations, such as:

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Get Support

The following support options can help you use Wildbook.

Open Source with a Professional Support Option

Wildbook® is always free and open source. We are a community of IT professionals and wildlife researchers maintaining and improving a 21st century platform. However, sometimes you may need extra help on a deadline. Our non-profit Wild Me offers professional hosting and customization to fit your project's requirements. This helps us fund our non-profit projects. Contact us if you need help!

People

Wildbook is a long-term, multi-disciplinary, multi-institution project combining skilled people in computer science, data science, ecology, and software engineering..

University of Illinois-Chicago

Professor Tanya Berger-Wolf provides computer science, data science, and overall project leadership to Wildbook.

Wild Me

www.wildme.org_wp-content_uploads_2015_09_1455109_10204334175773477_7845084325593654833_n1-300x300.jpg www.wildme.org_wp-content_uploads_2015_09_901250-300x300.jpg

Jason Holmberg is the Information Architect for Wildbook. Jon Van Oast, Drew Blount, and Colin Kingen are the primary software developers. Together we bring a wealth of professional software engineering experience to Wildbook.

Rensselaer Polytechnic Institute

Professor Charles Stewart and his students provide artificial intelligence and computer vision research and technology to Wildbook.

Princeton University

Professor Dan Rubenstein provides ecology and biology guidance to Wildbook as well as field testing Kenya.

Other

Dr. Scott Baker of Oregon State University designed the DNA-related components within the software and remains an active adviser on the project.

Sponsors

Ongoing support for Wildbook is funded by the National Science Foundation, Amazon Web Services, collaborative co-investment by users, and donations to Wild Me.

Past development work for Wildbook has been supported by:

Screenshots

Donation

You can help move Wildbook forward by making a donation! Your donation is tax deductible in the United States.

Publications

The following publications have resulted from Wildbook-related work:

  • S. Menon, T. Y. Berger-Wolf , E. Kiciman, L. Joppa, C. V. Stewart, J. Parham, J. Crall, J. Holmberg, J. Van Oast, “Animal Population Estimation Using Flickr Images”, 2nd International Workshop on the Social Web for Environmental and Ecological Monitoring (SWEEM 2017) .
  • Araujo G, Lucey A, Labaja J, So CL, Snow S, Ponzo A. (2014) Population structure and residency patterns of whale sharks, Rhincodon typus, at a provisioning site in Cebu, Philippines. PeerJ 2:e543
  • Rohner CA, Pierce SJ, Marshall AD, Weeks SJ, Bennett MB, Richardson AJ (2013) Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks. Mar Ecol Prog Ser 482: 153–168, 2013.
  • McKinney J, Hoffmayer ER, Holmberg J, Graham R, de la Parra R et al. (2013) Regional connectivity of whale sharks demonstrated using photo-identification - Western Atlantic, 1999 - 2013. PeerJ PrePrints 1:e98v1
  • Bonner SJ & Holmberg, J (2013), Mark-Recapture with Multiple, Non-Invasive Marks. Biometrics. doi: 10.1111/biom.12045
  • Hueter RE, Tyminski JP, de la Parra R (2013) Horizontal Movements, Migration Patterns, and Population Structure of Whale Sharks in the Gulf of Mexico and Northwestern Caribbean Sea. PLoS ONE 8(8): e71883. doi:10.1371/journal.pone.0071883
  • Fox S, Foisy I, De La Parra Venegas R, Galvan Pastoriza BE, Graham RT, Hoffmayer ER, Holmberg J, Pierce SJ. (2013) Population structure and residency of whale sharks Rhincodon typus at Utila, Bay Islands, Honduras. Journal of Fish Biology Volume 83, Issue 3, pages 574-587, September 2013
  • Robinson DP, Jaidah MY, Jabado RW, Lee-Brooks K, Nour El-Din NM, et al. (2013) Whale Sharks, Rhincodon typus, Aggregate around Offshore Platforms in Qatari Waters of the Arabian Gulf to Feed on Fish Spawn. PLoS ONE 8(3): e58255. doi:10.1371/journal.pone.0058255
  • Davies, Tim K., Stevens, Guy, Meekan, Mark G., Struve, Juliane, and Rowcliffe, J. Marcus (2012) Can citizen science monitor whale-shark aggregations? Investigating bias in mark-recapture modelling using identification photographs sourced from the public. Wildlife Research 39, 696-704.
  • Marshall AD & SJ Pierce (2012) The use and abuse of photographic identification in sharks and rays. Journal of Fish Biology 80: 1361-1379

Design Influences

The following publications have influenced our design and development of Wildbook:

Wildbook is a registered trademark of Wild Me.

Wildbook is distributed under the GPL v2 open source license.