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Sarah Dalrymple added a new Discussion - "Small mammal detection" to Acoustics
Sarah Dalrymple added a new Discussion - "Training datasets for counting Grey Seals in UAV imagery" to AI for Conservation
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Used to pick up signals from tracking gear on the ground, collect images of wildlife and habitats from the air, gather acoustic data with specialized hydrophones, or even collect snot samples from whales' blowholes, drones are capable of collecting high-resolution data quickly, noninvasively, and at relatively low cost.
🌍 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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- 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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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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Sarah Dalrymple's Content
Discussion
Small mammal detection
Sarah Dalrymple
Is anyone (other than BTO's Acoustic Pipeline) working on detection and classification of ultrasonic vocalisations of small mammals? We're looking at automatic detection of...
21 February 2025
Discussion
Training datasets for counting Grey Seals in UAV imagery
Sarah Dalrymple
Does anyone have training datasets for (grey seal) object detection in UAV images, or be suggest where I might be able to find some?
21 October 2024
Discussion
Audiomoth and Natterjack Monitoring (UK)
Sarah Dalrymple
Is anyone aware of anything that has been published, or anyone who is using Audiomoth (or similar) to monitor Natterjack toad calling at night? Or using it to monitor any night-...
23 December 2020
Sarah Dalrymple commented on "Small mammal detection"