Lorène Jeantet

Thrilled to showcase how artificial intelligence can support wildlife monitoring and conservation ! 🤗 🐧 🐢

🎉 Last day at AIMS for our student Matthew (co-supervised with Emmanuel Dufourq)! He presented his 2-year project: a lightweight pose estimation model to monitor African penguins 🐧 + custom Raspberry Pi camera system for remote footage capture 🎥
Amazing work — well done Matthew! 👏 #AI #Conservation

Hello Sara, can I also be added please 🤗? thank you !

Hello, can you add me to the pack ? thank you !

research_za/biologging_transferlearning_hawksbill at main · AIMS-Research/research_za Research at AIMS South Africa. Contribute to AIMS-Research/research_za development by creating an account on GitHub.

All the data is available online, and the code is on GitHub as notebooks to make it easy to use !
👉 github.com/AIMS-Researc...

This study was part of Kukanya Zondo's research project for his Master's in Mathematical Sciences at AIMS South Africa
, where he graduated with distinction— congratulations! 💪

Transfer learning helps study endangered species with deep learning, using data from different species or humans when specific data is scarce. It works across architectures and allows reuse of online available models , usually provided with the data they were trained on.

Thanks to Damien Chevallier and the CNRS team, 6 hawksbill turtles were equipped with onboard cameras, accelerometers, gyroscopes, and pressure sensors—enabling accelerometer signal validation. A first for this species!

Pre-training on data from green turtles or humans boosts accelerometer-based behavior identification for hawksbill turtles. Fine-tuning the model outperforms training solely on hawksbill data.

🚩 New paper published in JEB !
journals.biologists.com/jeb/article/...
We test transfer learning with deep learning to automatically identify the behaviors of endangered species like Hawksbill sea turtle using accelerometer data.

GitHub - jeantetlorene/Vnet_seaturtle_behavior Contribute to jeantetlorene/Vnet_seaturtle_behavior development by creating an account on GitHub.

All the code is available on GitHub => github.com/jeantetloren...

With minimal preprocessing, F1-score of 81.1% and Global accuracy of 97.2%. The 6 behavioral categories identified: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming.

🚩 🚩Improving ecological knowledge of sea turtles
V-net : a new method based on deep learning to infer sea turtle behavior from multi-sensor loggers

www.sciencedirect.com/science/arti...