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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>

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• Video: Delivering edge computing on Robinson Crusoe Island (Chile) to preserve biodiversity 

David Will, Head of Innovation @ Island Conservation & Charles Ferland, VP & GM of Edge Computing & Communication Service Providers @ Lenovo

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• EarthRanger Announces Inaugural Conservation Tech Award Recipients

<p>On 3rd November 2021, Earthranger Announced <a href="https://giraffeconservation.org/">Giraffe Conservation Foundation </a>and<a href="http://lionguardians.org/"> Lion Guardians</a> as the inaugral Conservation Tech Award Recipients. The two organizations are Harnessing the Power of Technology to Protect Endangered Species and Promote Human-Wildlife Coexistence.</p>

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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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• New Paper: Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory

This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios.

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• New microprocessor for low power AI camera & audio applications from Microchip

Microchip has just announced the 1 GHz SAMA7G54 single-core Arm Cortex-A7 microprocessor (MPU) with MIPI CSI-2 and parallel camera interfaces, as well as up to four I2S, one SPDIF transmitter and receiver, and a 4-stereo channel audio sample rate converter.