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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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• Introducing the WILDLABS On the Edge Fellows

<p>We're proud to introduce the first <strong>WILD</strong>LABS On the Edge Fellows for 2022, Loretta Schindlerova and Meredith Palmer! Working alongside expert Edge Impulse mentors, these two fellows will use embedded machine learning to bring innovative projects to life.</p>