This tutorial builds a runnable, end-to-end version of the Lkotkote bioacoustic monitoring pipeline, which combines AI-based bird sound identification with Samburu Traditional Ecological Knowledge (TEK) for climate-relevant biodiversity monitoring in northern Kenya. By the end, users will be able to take TEK-informed field sites and audio recordings through automated detection, AI species identification, community TEK enrichment, and open Darwin Core/GBIF publishing — and will understand where each real-world tool (AudioMoth, Raven Lite, Arbimon, BirdNET-Analyzer, the Lkotkote platform) plugs into that pipeline.
Author:
- Douglas Mbura, Co-founder, Lkotkote, Nairobi, Kenya
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.
Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Mbura, D. (2026). A Bird Bioacoustic Monitoring Pipeline — The Lkotkote Case Study [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.21927837
@misc{mbura2026bird,
title={A Bird Bioacoustic Monitoring Pipeline — The Lkotkote Case Study},
author={Mbura, Douglas},
year={2026},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.21927837},
booktitle={Climate Change AI Summer School},
howpublished={\url{https://github.com/climatechange-ai-tutorials/lkotkote-bird-bioacoustics}}
}