Publications

Below is list of publications from Conservation AI, showcasing our latest research and innovations in using artificial intelligence for wildlife conservation and biodiversity monitoring. These publications highlight our commitment to advancing technology-driven solutions to protect endangered species and ecosystems worldwide.

    1. Fergus, Paul, Carl Chalmers, Steven Longmore, and Serge Wich. 2024. “Harnessing Artificial Intelligence for Wildlife Conservation” Conservation 4, no. 4: 685-702.
    2. Kissling, W.D., Evans, J.C., Zilber, R., Breeze, T.D., Shinneman, S., Schneider, L.C., Chalmers, C., Fergus, P., Wich, S. and Geelen, L.H., 2024. Development of a cost-efficient automated wildlife camera network in a European Natura 2000 site. Basic and Applied Ecology, 79, pp.141-152.
    3. Thompson, T., Chalmers, C., Muller, J., Fergus, P., Orsini, L., Zhou, J., 2023. How AI can help to save endangered species. Nature, 623(7986), pp.232-233.
    4. Chalmers, C., Fergus, P., Wich, S., Longmore, S.N., Walsh, N.D., Stephens, P.A., Sutherland, C., Matthews, N., Mudde, J. and Nuseibeh, A., 2023. Removing human bottlenecks in bird classification using camera trap images and deep learning. Remote Sensing, 15(10), p.2638.
    5. Fergus, P., Chalmers, C., Longmore, S., Wich, S., Warmenhove, C., Swart, J., Ngongwane, T., Burger, A., Ledgard, J. and Meijaard, E., 2023. Empowering wildlife guardians: an equitable digital stewardship and reward system for biodiversity conservation using deep learning and 3/4G camera traps. Remote Sensing, 15(11), p.2730.
    6. Vélez, J., McShea, W., Shamon, H., Castiblanco‐Camacho, P.J., Tabak, M.A., Chalmers, C., Fergus, P. and Fieberg, J., 2023. An evaluation of platforms for processing camera‐trap data using artificial intelligence. Methods in Ecology and Evolution, 14(2), pp.459-477.
    7. Doull, K.E., Chalmers, C., Fergus, P., Longmore, S., Piel, A.K. and Wich, S.A., 2021. An evaluation of the factors affecting ‘poacher’detection with drones and the efficacy of machine-learning for detection. Sensors, 21(12), p.4074.
    8. Vélez, J., Castiblanco-Camacho, P.J., Tabak, M.A., Chalmers, C., Fergus, P. and Fieberg, J., 2022. Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence. arXiv preprint arXiv:2202.02283.
    9. Piel, A.K., Crunchant, A., Knot, I.E., Chalmers, C., Fergus, P., Mulero-Pázmány, M. and Wich, S.A., 2022. Noninvasive technologies for primate conservation in the 21st century. International Journal of Primatology, pp.1-35.
    10. Chalmers, C., Fergus, P., Curbelo Montanez, C.A., Longmore, S.N. and Wich, S.A., 2021. Video analysis for the detection of animals using convolutional neural networks and consumer-grade drones. Journal of Unmanned Vehicle Systems, 9(2), pp.112-127.
    11. Chalmers, C., Fergus, P., Wich, S. and Longmore, S.N., 2021, July. Modelling animal biodiversity using acoustic monitoring and deep learning. In 2021 international joint conference on neural networks (IJCNN) (pp. 1-7). IEEE.
    12. Chalmers, C., Fergus, P., Wich, S. and Montanez, A.C., 2019. Conservation AI: Live stream analysis for the detection of endangered species using convolutional neural networks and drone technology. arXiv preprint arXiv:1910.07360.