Benjamin Kellenberger, PhD
Areas of Interest
I am a data scientist with background in Earth observation, computer vision and machine learning. In my previous work at EPFL Switzerland I devised methods to automatically detect and count animals in very high-resolution aerial imagery using deep learning. My current research attempts to make sense not just of where species individuals are, but why; to this end I research on the intersection of data science and ecology to augment our understanding and capabilities of species distribution modeling at scale.
Cello, Karate, hiking, learning languages
Tuia, D, B Kellenberger, S Beery, BR Costelloe, ... , T Berger-Wolf. 2022. Perspectives in Machine Learning for Wildlife Conservation. Nature Communications. https://doi.org/10.1038/s41467-022-27980-y
Kellenberger B, D Tuia, D Morris. 2020. AIDE: Accelerating Image-based Ecological Surveys with Interactive Machine Learning. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13489
Kellenberger B, D Marcos, D Tuia. 2018. Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.06.028
Palminteri S. (August 2, 2018). "Automating drone-based wildlife surveys saves time and money, study finds." Mongabay. https://news.mongabay.com/2018/08/automating-drone-based-wildlife-surveys-saves-time-and-money-study-finds/
EPFL Research Office. (November 11, 2022). "Zeno Karl Schindler Award - 2022 - Benjamin Kellenberger." École Polytechnique Fédérale de Lausanne. https://actu.epfl.ch/news/zeno-karl-schindler-award-2022-benjamin-kellenberg/
Sprick M, et al. (July 11, 2018). "Drohnen Zählen in Afrika nashörner und gnus: NZZ." Neue Zürcher Zeitung. https://www.nzz.ch/wissenschaft/drohnen-zaehlen-in-afrika-nashoerner-und-gnus-ld.1402588
