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The East Africa Community is WILDLABS first regional hub. This group creates a space to foster connections and collaboration between conservation and technology players in the Silicon Savannah.
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- Introducing the 2026 Cohort: Women in Conservation Technology Programme, Tanzania 2026
We're delighted to announce the launch of our fourth Women in Conservation Technology Programme, in partnership with the Grumeti Fund and with support from the JRS Biodiversity Foundation. Join us in welcoming the 2026 cohort as they kick off the training with an in-person workshop in Tanzania this July!
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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.
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- Deep Voice - A Free Online Platform for AI-Based Marine Mammal Sound Detection and Classification
Passive acoustic monitoring floods marine researchers with data that can take months to annotate by hand, and the AI models that could help have long required Python setup, GitHub repos, and complex config files. Funded by the WILDLABS Awards 2025, Deep Voice removes that barrier with a free, public web app that turns marine mammal sound detection into a simple drag-and-drop task.
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- Loaning Bioacoustics Recorders
Acoustic is one of our biggest and most active groups, with members collecting, analysing, and interpreting acoustic data from across species, ecosystems, and applications, from animal vocalizations to sounds from our natural and built environment.
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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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Yuri Njathi commented on "Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation (v1.0)"