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.
Check out our video on National Geographic's Chasing Genius challenge: http://www.natgeochasinggenius.com/video/645
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.
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.
Wildbook ® is an open source software framework to support collaborative mark-recapture, molecular ecology, and social ecology studies, especially where citizen science data needs to be incorporated and managed. It is developed by the non-profit Wild Me (PI Jason Holmberg) in close collaboration with 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).
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:
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!
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.
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.
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.
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.
The following support options can help you use Wildbook.
Wildbook is used in public and private installations, such as:
Wildbook is a long-term, multi-disciplinary, multi-institution project combining skilled people in computer science, data science, ecology, and software engineering..
Professor Tanya Berger-Wolf provides computer science, data science, and overall project leadership to Wildbook.
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.
Professor Charles Stewart and his students provide artificial intelligence and computer vision research and technology to Wildbook.
Professor Dan Rubenstein provides ecology and biology guidance to Wildbook as well as field testing Kenya.
Dr. Scott Baker of Oregon State University designed the DNA-related components within the software and remains an active adviser on the project.
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:
Wildbook is designed to produce successful, reproducible collaborations between biologists, biostatisticians, computer scientists, and citizen scientists by providing a Web-based software platform for collaboration.
If you answer yes to any of these questions, Wildbook may be a very good choice for your research.
Using the web-based interface of Wildbook Framework, a research team can:
Encounters (a.k.a. “captures” or “sightings”)
The framework is open source and meant for you to extend it for your specific project! If it doesn't have the feature you need, use some simple Java programming and create it. Some things we have used it to do on whaleshark.org are:
The development of additional functionality is currently underway.
You can help move Wildbook forward by making a donation! Your donation is tax deductible in the United States.
Please send feedback to jason at whaleshark dot org. Your ideas to improve Wildbook are most welcome!
Wildbook was started by Jason Holmberg as the software behind the Wildbook for Whale Sharks, which is a multiuser, web-based, research application for studying whale sharks (Rhincodon typus). The aim of WIldbook for Whale Sharks is to prevent individual “silos” of whale shark data and to promote a global, cooperative approach to whale shark research using the Web as a communications and research platform. The Library went on-line and began collecting whale shark encounter data from the web in January 2003. In early 2004, the pattern-recognition system that allows the Library to distinguish between individual whale sharks using natural spot patterning was integrated. Since its first line of code, this Wildbook has seen continuous feature additions, bug fixes, and performance enhancements. Our work to maintain and enhance the Library is ongoing and requires knowledge of Java, J2EE, JDO, PHP , Flash/Flex, HTML , XML , RSS , a wee bit of Python, and CSS .
The following publications have resulted from Wildbook-related work:
The following publications have influenced our design and development of Wildbook: