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• WILDLABS State of Conservation Tech Survey 2022

Last year, WILDLABS published the first global, community-sourced assessment of the State of Conservation Technology, which evolved from our annual community survey. Take the 2022 survey to help us build from this baseline to track trends and shape the future of our rapidly evolving community and sector.

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• First-of-Its-Kind Digital Platform Reveals Migration Data for Birds Across the Americas Just In Time for Fall Migration

Interactive digital platform visualizes the incredible journeys of migratory birds, how they connect us across the hemisphere, and the widespread challenges they face throughout their full annual cycle.

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• Identification of Wildlife in Camera Trap Images

<p>Camera trap wildlife surveys can generate vast amounts of imagery. A key problem in the wildlife ecology field is that vast amounts of time is spent reviewing this imagery to identify the species detected. Valuable resources are wasted, and the scale of studies is limited by this review process. The use of computer software capable of extracting false positives, automatically identifying animals detected and sorting imagery could greatly increase efficiency. AI has been demonstrated as an effective option for automatically identifying species from camera trap imagery. Currently available code bases are inaccessible to the majority of users; requiring high-performance computers, advanced software engineering skills and, often, high-bandwidth internet connections to access cloud services. The <em>ClassifyMe</em> software tool is designed to address this gap and provides users the opportunity to utilise state-of-the-art image recognition algorithms without the need for specialised computer programming skills. <em>ClassifyMe</em> is IDEAL for field researchers, allowing users to sweep through camera trap imagery using field computers instead of office-based workstations.</p>

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• Module 8: In the Field

Welcome to the eighth and final module of our Build Your Own Data Logger virtual course. We’ve built, coded and tested our data logger. Now we’re taking it into the field.