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As our collaborations and effort grew, it quickly became apparent that our work would be unprecedented. We were the first software engineers to bring wildlife computer vision into the browser and to make both the database and the matching system available to multiple users simultaneously. All this supports new levels of collaboration and important efforts to match individuals across migratory routes.
With the online release of spot patterning matching for whale sharks in 2005 (now Sharkbook.ai), the foundations of Wild Me’s work were launched. From tall giraffes to tiny seadragons, we now provide free online platforms for many of the world’s marine and terrestrial wildlife researchers. These allow them to use multiple forms of advanced computer vision and machine learning to track individual animals across borders and data sets in support of population analysis. Armed with reliable population numbers, we seek to support locally-led, rapidly iterating, and data-driven conservation strategies that restore vulnerable wildlife populations to healthy levels.
This data has already been used in a growing list of independent scientific publications and in support of local research, citizen science, and conservation efforts.
Yet we have also know that we’re only serving a small portion of the research efforts and species that we could be. From cassowaries and koalas to tiger sharks and wolves, the community requests for our help continue to exceed our resources. As we close out 2022, I am writing to ask: can you help us grow our impact in 2023?
Wild Me is a nonprofit organization with full tax deductibility in the United States, and we are staffed by critically needed software and machine learning professionals directly supporting local wildlife research efforts across the globe. Your generosity and support can help us bring cutting-edge technology and global-scale collaboration to the front-line research and conservation efforts that can turn the tide against extinction.
Please visit our donation page to support Wild Me.
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Back at the beginning, our very first challenge was to find an algorithm to match whale sharks between photos using their spot patterns. But this initial volunteer exploration was deceivingly simple, even though it took three people another three years to complete. Computer vision to find and match individual animals in photographs is not useful as lines of code. Matching systems must be accessible, functional, cost-effective, and interpretable in order to be adopted by the wildlife research community, which is very poorly funded. 