Benjamin Kellenberger - Publications

Zbinden, R., van Tiel, N., Kellenberger, B., Hughes, L., Tuia, D.: On the Selection and Effectiveness of Pseudo-Absences for Species Distribution Modeling with Deep Learning. Ecological Informatics 81, 2024.
Ecological Informatics—open access

@article{zbinden2024selection,
    title={On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning},
    author={Zbinden, Robin and van Tiel, Nina and Kellenberger, Benjamin and Hughes, Lloyd and Tuia, Devis},
    journal={Ecological Informatics},
    volume={81},
    pages={102623},
    year={2024},
    publisher={Elsevier}
}

Rußwurm, M., Wang, S., Kellenberger, B., Roscher, R., Tuia, D.: Meta-Learning to Address Diverse Earth Observation Problems Across Resolutions. Nature Communications Earth & Environment 5(1), 2024.
Nature Communications Earth and Environment—open access

@article{russwurm2024meta,
    title={Meta-learning to address diverse Earth observation problems across resolutions},
    author={Ru{\ss}wurm, Marc and Wang, Sherrie and Kellenberger, Benjamin and Roscher, Ribana and Tuia, Devis},
    journal={Communications Earth \& Environment},
    volume={5},
    number={1},
    pages={37},
    year={2024},
    publisher={Nature Publishing Group UK London}
}

Tuia, D.*, Kellenberger, B.*, Beery, S.*, Costelloe, BR*, Zuffi, S., Risse, B., Mathis, A., Mathis, MW, van Langevelde, F., Burghardt, T., Kays, R., Klinck, H., Wikelski, M., Couzin, ID, van Horn, G., Crofoot, MC, Stewart, CV, Berger-Wolf, T.: Perspectives in Machine Learning for Wildlife Conservation. Nature Communications, 2022.
* equal contribution
Position Paper at Nature Communications—open access

@article{
    title={Perspectives in Machine Learning for Wildlife Conservation},
    author={Tuia, Devis and Kellenberger, Benjamin and Beery, Sara and Costelloe, Blair R and Zuffi, Silvia and Risse, Benjamin and Mathis, Alexander and Mathis, Mackenzie W and van Langevelde, Frank and Burghardt, Tilo and Kays, Roland and Klinck, Holger and Wikelski, Martin and Couzin, Iain D and van Horn, Grant and Crofoot, Margaret C and Stewart, Charles V and Berger-Wolf, Tanya},
    journal={Nature Communications},
    volume={13},
    issue={792},
    year={2022},
    publisher={Nature Publishing Group}
}

Hoekendijk, J., Kellenberger, B.*, Aarts, G., Brasseur, S., Poiesz, SSH, Tuia, D.: Counting Using Deep Learning Regression gives Value to Ecological Surveys. Nature Scientific Reports, 2021.
Nature Scientific Reports—open access

@article{
    title={Counting using deep learning regression gives value to ecological surveys},
    author={Hoekendijk, Jeroen and Kellenberger, Benjamin and Aarts, Geert and Brasseur, Sophie and Poiesz, Suzanne SH and Tuia, Devis},
    journal={Scientific reports},
    volume={11},
    number={1},
    year={2021},
    publisher={Nature Publishing Group}
}

Kellenberger, B.*, Tasar, O., Damodaran, BB, Courty, N., Tuia, D.: Deep Domain Adaptation in Earth Observation. In: Camps-Valls, G., Tuia, D., Zhu, XX, Reichstein, M. (eds.): Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences. Wiley Publishing Group, 2021.
Book chapter—link—GitHub

@inbook{
    chapter={Deep Domain Adaptation in Earth Observation},
    author={Kellenberger, Benjamin and Tasar, Onur and Bhushan Damodaran, Bharath and Courty, Nicolas and Tuia, Devis},
    title={Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences},
    editor={Camps-Valls, Gustau and Tuia, Devis and Zhu, Xiao Xiang and Reichstein, Markus},
    chapter={7},
    pages={90--104},
    year={2021},
    doi={https://doi.org/10.1002/9781119646181.ch7},
    url={https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch7},
    eprint={https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch7},
    publisher={John Wiley & Sons, Ltd},
    isbn={9781119646181},
}

Kellenberger, B., Veen, T., Folmer, E., Tuia, D.: 21 000 Birds in 4.5 h: Efficient Large-scale Seabird Detection with Machine Learning. Remote Sensing in Ecology and Conservation, 2021. Journal Article at RSEC—PDF

@article{
    title={21 000 birds in 4.5 h: efficient large-scale seabird detection with machine learning},
    author={Kellenberger, Benjamin and Veen, Thor and Folmer, Eelke and Tuia, Devis},
    journal={Remote Sensing in Ecology and Conservation},
    year={2021},
    publisher={Wiley Online Library}
}

Kellenberger, B., Tuia, D., Morris, D.: AIDE: Accelerating Image-based Ecological Surveys with Interactive Machine Learning. Methods in Ecology and Evolution, 2020. Journal Article at MEE—PDF—GitHub

@article{
    title={{AIDE}: Accelerating image-based ecological surveys with interactive machine learning},
    author={Kellenberger, Benjamin and Tuia, Devis and Morris, Dan},
    journal={Methods in Ecology and Evolution},
    volume={11},
    number={12},
    pages={1716--1727},
    year={2020},
    publisher={Wiley Online Library}
}

Kellenberger, B., Marcos, D., Lobry, S., Tuia, D.: Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning. IEEE Transactions on Geoscience and Remote Sensing, 2019. Journal Article at IEEE TGRS—PDF

@inproceedings{
    title={Half a Percent of Labels is Enough: Efficient Animal Detection in {UAV} Imagery using Deep {CNNs} and Active Learning},
    author={Kellenberger, Benjamin and Marcos, Diego and Lobry, Sylvain and Tuia, Devis},
    booktitle={IEEE Transactions on Geoscience and Remote Sensing},
    year={2019}
}

Kellenberger, B., Marcos, D., Tuia, D.: When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. Oral at CVPR (EarthVision workshop)—best student paper award—PDF

@inproceedings{
    title={When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in {UAV} Images},
    author={Kellenberger, Benjamin and Marcos, Diego and Tuia, Devis},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
    year={2019}
}

Damodaran, BB, Kellenberger, B., Flamary, R., Tuia, D.: DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation. European Conference on Computer Vision (ECCV), 2018. Poster at ECCV (joint first author)—PDF

@inproceedings{
    title={DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation},
    author={Bhushan Damodaran, Bharath and Kellenberger, Benjamin and Flamary, R{\'e}mi and Tuia, Devis and Courty, Nicolas},
    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
    pages={447--463},
    year={2018}
}

Kellenberger, B., Marcos, D., Tuia, D.: Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning. Remote Sensing of Environment, 2018. Journal Article at Remote Sensing of Environment—ScienceDirect—PDF (pre-print)

@article{
    title={Detecting mammals in {UAV} images: Best practices to address a substantially imbalanced dataset with deep learning},
    author={Kellenberger, Benjamin and Marcos, Diego and Tuia, Devis},
    journal={Remote sensing of environment},
    volume={216},
    pages={139--153},
    year={2018},
    publisher={Elsevier}
}

Marcos, D., Tuia, D., Kellenberger, B., Zhang, L., Bai, M., Liao, R., Urtasun, R.: Learning Deep Structured Active Contour Models End-to-end. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. Spotlight at CVPR—PDF (pre-print)

@inproceedings{
    title={Learning deep structured active contours end-to-end},
    author={Marcos, Diego and Tuia, Devis and Kellenberger, Benjamin and Zhang, Lisa and Bai, Min and Liao, Renjie and Urtasun, Raquel},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={8877--8885},
    year={2018}
}

Marcos, D., Volpi, M., Kellenberger, B., Tuia, D.: Land Cover Mapping at Very High Resolution with Rotation Equivariant CNNs: Towards Small yet Accurate Models. ISPRS Journal of Photogrammetry and Remote Sensing, 2018. Journal Article—PDF (pre-print)

@article{
    title={Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models},
    author={Marcos, Diego and Volpi, Michele and Kellenberger, Benjamin and Tuia, Devis},
    journal={ISPRS journal of photogrammetry and remote sensing},
    volume={145},
    pages={96--107},
    year={2018},
    publisher={Elsevier}
}