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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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• 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. 

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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 article: SMART for wildlife health surveillance

Author: Diego Montecino

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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.

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• Detection dogs provide a powerful method for conservation surveys

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• Bird Sounds Global web portal

A new web portal for annotating bird sounds has been opened called Bird Sounds Global (BSG). It is part of project LIFEPLAN, and its key objective is to develop global, automated software for bird sound identification.

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• New papers on passive acoustic monitoring

PAM & transfer learning, defining occupancy in surveys, effects of anthropogenic noise on fish, soundscapes to track ecosystem recovery, detecting sharks with echosounders, using PAMGuard to detect marine mammals 

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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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• Conservation Technology and the Supply Chain

"To paraphrase Hemingway, the chip shortage hit FieldKit in two ways: gradually and then suddenly."

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• WILDLABS Virtual Meetup: Future Questions & Tools in Movement Ecology (Recording)

Our fourth and final meetup in Season 4 explored the future of movement ecology, including tools that could change the game and questions we might not yet have thought to ask. On June 8, we heard short talks from leading experts Christian Rutz, Ran Nathan, Martin Wikelski, and Tanya Berger-Wolf, followed by open discussion and community exchange.