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- AI for Impact Series at WWF: Looking for experts/speakers
Artificial intelligence is increasingly being used in the field to analyse information collected by wildlife conservationists, from camera traps and satellite images to audio recordings. AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings - hugely reducing the manual labour required to collect vital conservation data. The AI For Conservation group is intended to unite and inspire all WILDLABS community members—whether already involved in AI for conservation, or not—to understand how to use and/or directly contribute to open-source research and development efforts.
🌍 Conservation technology is transforming how we protect wildlife, but are we thinking carefully enough about the risks? Drones, camera traps, GPS trackers, acoustic sensors, AI, and remote sensing have become essential tools for conservation practitioners around the world. They help us monitor species, detect threats, and respond faster than ever before. But these same technologies can also introduce unintended risks, and in some cases, can be exploited by those seeking to harm the very wildlife we're trying to protect. 🦏 Input now and/or join the discussions/research.
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- Custom Hydrophone Records Dolphins
Welcome to the official group forum for our virtual course, Build Your Own Data Logger. This is your space to engage with course instructors Akiba and Jacinta from Freaklabs, find help and resources for each module, collaborate and chat with your fellow course participants, and share your progress on your own Data Logger project!
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- Open-Source Solutions for Amphibian Passive Acoustic Monitoring: Lessons from Patagonia
Monitoring amphibians across the temperate forests of Patagonia presents significant logistical and technical challenges. Remote locations, harsh environmental conditions, and the large volumes of data generated by Passive Acoustic Monitoring (PAM) can make long-term biodiversity surveys difficult to implement and maintain. In addition, environmental data often relies on multiple independent devices, increasing costs, complexity, and logistical demands in remote field conditions. Through the WILDLABS Awards 2025, our team explored practical ways to address these challenges by combining open-source hardware, environmental sensing, and AI-assisted acoustic analysis.
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